spb/wp44_uqo

1{"work_id": 3134501457, "title": "KLASIFIKASI INDEKS PEMBANGUNAN MANUSIA KABUPATEN/KOTA DI INDONESIA MENGGUNAKANMETODE RANDOM FOREST", "abstract": "Keberhasilan suatu negara dapat dilihat dari kondisi masyarakatnya. Suatu negara\r\nperlu mengembangkan masyarakat, yaitu dengan cara pembangunan manusia.\r\nPembangunan manusia terdiri dari tiga komponen dasar yaitu umur panjang dan\r\nsehat; pengetahuan dan kehidupan yang layak. Beberapa indikator yang mewakili\r\nketiga komponen tersebut diringkas menjadi satu nilai tunggal yaitu Indeks\r\nPembangunan Manusia. Oleh karena itu, untuk Random Forest merupakan\r\npengembangan dari metode Classification And Regression Trees (CART) dengan\r\nmenambahkan proses bagging, yang digunakan adalah resampling bootstrap.\r\nMetode ini mampu meningkatkan kestabilan pohon klasifikasi yang terbentuk dan\r\nakurasi prediksi yang dihasilkan. Penelitian ini akan mengklasifikasikan IPM\r\nkabupaten/kota di Indonesia dengan jumlah 514 kabupaten/kota. Variabel respon\r\ndalam penelitian ini adalah indeks pembangunan manusia dan variabel\r\nprediktornya adalah angka harapan hidup, rata-rata lama sekolah, harapan lama\r\nsekolah, dan pengeluaran perkapita yang disesuaikan. Berdasarkan analisis\r\nmenggunakan Random Forest didapatkan hasil bahwa banyaknya pohon yang\r\nterpilih adalah 100 dengan m yang dicobakan adalah 2 dan variabel yang paling\r\nberpengaruh dalam kenaikan Indeks Pembangunan Manusia adalah Pendapatan\r\nPer Kapita dengan nilai kepentingan sebesar 29.48. Ketepatan hasil klasifikasi\r\nyang diukur menggunakan Total Accuracy Rate (1-APER) menghasilkan nilai\r\nakurasi sebesar 93.69%. \r\nKata Kunci: Indeks Pembangunan Manusia, Random Forest, Total Accuracy Rate\r\n(1-APER)."}2{"work_id": 5673758, "title": "A Novel Reconfiguration Scheme in Quantum-Dot Cellular Automata for Energy Efficient Nanocomputing", "abstract": "Quantum-Dot Cellular Automata (QCA) is currently being investigated as an alternative to CMOS technology. There has been extensive study on a wide range of circuits from simple logical circuits such as adders to complex circuits such as 4-bit processors. At the same time, little if any work has been done in considering the possibility of reconfiguration to reduce power in QCA devices. This work presents one of the first such efforts when considering reconfigurable QCA architectures which are expected to be both robust and power efficient. We present a new reconfiguration scheme which is highly robust and is expected to dissipate less power with respect to conventional designs. An adder design based on the reconfiguration scheme will be presented in this thesis, with a detailed power analysis and comparison with existing designs. In order to overcome the problems of routing which comes with reconfigurability, a new wire crossing mechanism is also presented as part of this thesis."}3{"work_id": 3156168805, "title": "Ultra-low vibration closed-cycle cryogenic surface-electrode ion trap apparatus", "abstract": "Trapped ions are one of the leading platforms for building scalable quantum information processors. Current ion traps run on less than 100 qubits. A useful quantum computer needs to operate thousands or even millions of qubits. If systems reach gate fidelities that allow for quantum error correction, gate errors below the physical fidelity can be reached, which is important for scaling. The most promising platform for scaling up ion-based quantum computers are surface electrode traps in connection with the so-called QCCD (Quantum Charge Coupled Device) architecture. In an evolution of the standard (laser-based) approach to quantum logic gates, gates can be performed by applying an oscillating magnetic near-field gradient to the ions. Current fidelities are about one order of magnitude away from reaching the fault tolerant threshold with no fundamental limitations in sight. Scaling QCCDs is impeded by heating rates of the ions as well as vacuum quality. Ions have to be stored for at least as long as it takes for an algorithm to run and subsequent readout. Collisions with background gas and heating can expel ions from the trap. The microwave near-field approach of driving gates benefits from ions that are trapped close to the trap surface. Unfortunately, heating becomes more prominent near the surface. A cryogenic environment reduces the background pressure by several orders of magnitude and the kinetic energy of gas molecules by two orders of magnitude. Heating rates in a cryogenic trap are about two orders of magnitude lower than in a room temperature trap with otherwise equal physical properties. To operate a QCCD processor, it is required to employ phase-stable Raman laser beams. The cryogenic design of the apparatus needs to take into account the resulting requirements for ultra-low vibration amplitudes. This is particularly relevant for closed-cycle cooling systems, which are desirable from an economic point of view compared to liquid cryostats that boil off helium, but which also feature moving mechanical parts that can introduce a serious amount of vibrations. Here, we present a vibration isolated closed cycle Gifford-McMahon cryocooler with record low vibration amplitudes of 29 nm (RMS=7.8 nm) in the vertical and 51 nm (RMS=13.5 nm) in the horizontal direction. It contains a self-sustained inner vacuum chamber that houses the trap. It features 100 DC connections, 10 of which are able to carry up to 1 A of current and 8 high frequency lines. At 5 K, we can apply continuous 3.4 W of microwave power continuously at 1 GHz to the trap electrodes without heating the system. This is about 5.8 times more than state of the art near-field gates require. We also present the laser systems required to for single-shot ablation loading and Doppler cooling of Be ions: a 70 mW 313 nm Dopper cooling laser, a 1064 nm ns-pulse laser for ablation of neutral beryllium atoms and a 235 nm laser for ionizing them. The detection system features an in vacuum imaging system on a cryogenic 3D translation stage that is able to scan and image the complete surface of an ion trap. The light can be detected on a photomultiplier tube or spatially resolved on an EMCCD camera. Our trap features a meander structure for driving gates and can trap up to 5 ions of which we, so far, did not lose any due to background gas collisions. This hints at a vacuum pressure of 1e-12 mbar or lower. The demonstrated system could hold a cryogenic QCCD chip with a few 10 qubits"}4{"work_id": 4404401018, "title": "PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU", "abstract": "This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key principle underlying the design of PowerInfer is exploiting the high locality inherent in LLM inference, characterized by a power-law distribution in neuron activation. This distribution indicates that a small subset of neurons, termed hot neurons, are consistently activated across inputs, while the majority, cold neurons, vary based on specific inputs. PowerInfer exploits such an insight to design a GPU-CPU hybrid inference engine: hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons are computed on the CPU, thus significantly reducing GPU memory demands and CPU-GPU data transfers. PowerInfer further integrates adaptive predictors and neuron-aware sparse operators, optimizing the efficiency of neuron activation and computational sparsity. The evaluation shows that PowerInfer significantly outperforms llama.cpp by up to 11.69\u00d7 while retaining model accuracy across various LLMs (including OPT-175B) on a single NVIDIA RTX 4090 GPU. For the OPT-30B model, PowerInfer achieves performance comparable to that of a high-end server-grade A100 GPU, reaching 82% of its token generation rate on a single consumer-grade RTX 4090 GPU."}5{"work_id": 7128511066, "title": "The impacts of globalization, institutional quality, and digital technology innovation on OECD countries sustainable digital economic growth", "abstract": "This study examines the effects of the digital economy components (digital technology innovation), institutional quality, and globalization on achieving sustainable digital economic growth within the Organization for Economic Cooperation and Development. Using panel data from 2002 to 2020 and applying the Generalized Method of Moments, the cointegration results confirm the presence of cointegration among all variables. The empirical findings reveal that digital technology innovation and institutional quality significantly enhance economic growth, with positive contributions of 0.014% and 0.011%, respectively. Meanwhile, globalization contributes by 0.533%. The study concludes that technological innovation, institutional quality, and globalization played a significant role in the country's economic expansion. The green total factor productivity shows a positive contribution. The Dumitrescu-Hurlin panel causality test results indicate two-way relationships among economic growth (GDP), labor, capital, institutional quality, and globalization. According to this analysis, selected nations can achieve the Sustainable Development Goals by implementing the necessary measures to achieve a balanced approach to sustainability across economics, society, and the environment. This can be accomplished through digital technology innovations, green technologies, and robust institutions. Integrating globalization and digitalization through increased investment in digital infrastructure would enhance access to innovative technologies and connectivity, while additionally improving efficiency."}6{"work_id": 4411368489, "title": "Making AI Less 'Thirsty'", "abstract": "Uncovering and addressing the secret water footprint of AI models"}7{"work_id": 2982169647, "title": "Quantum supremacy using a programmable superconducting processor", "abstract": "The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor1. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits2\u20137 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253 (about 1016). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times\u2014our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy8\u201314 for this specific computational task, heralding a much-anticipated computing paradigm. Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute."}8{"work_id": 2916516729, "title": "SLM-DB: Single-Level Key-Value Store with Persistent Memory", "abstract": "This paper investigates how to leverage emerging byteaddressable persistent memory (PM) to enhance the performance of key-value (KV) stores. We present a novel KV store, the Single-Level Merge DB (SLM-DB), which takes advantage of both the B+-tree index and the Log-Structured Merge Trees (LSM-tree) approach by making the best use of fast persistent memory. Our proposed SLM-DB achieves high read performance as well as high write performance with low write amplification and near-optimal read amplification. In SLM-DB, we exploit persistent memory to maintain a B+-tree index and adopt an LSM-tree approach to stage inserted KV pairs in a PM resident memory buffer. SLM-DB has a single-level organization of KV pairs on\\ndisks and performs selective compaction for the KV pairs, collecting garbage and keeping the KV pairs sorted sufficiently for range query operations. Our extensive experimental study demonstrates that, in our default setup, compared to LevelDB, SLM-DB provides 1.07 - 1.96 and 1.56 - 2.22 times higher read and write throughput, respectively, as well as comparable range query performance."}9{"work_id": 4295093682, "title": "fast16 as a Second-Order Subversion Attack", "abstract": "SentinelOne's April 2026 disclosure of fast16, a precision sabotage framework compiled in August 2005, predates Stuxnet by five years and corrupts floating-point calculations in engineering simulation software via kernel-mode filesystem filtering. The infosec community described the mechanism accurately but did not classify the attack class. This paper applies the nth-order attackframework from Bilar (NATO CCDCOE, 2009) and argues fast16 is a second-order subversion attack: the high-precision research end system is reached through two successive ancillary systems, the OS kernel filesystem stack and the engineering simulation software above it. The wormlet is a delivery mechanism, not a distinct ancillary hop. fast16 is structurally comparable to Thompson's compiler trojan but one order lower and more covert: it operates at runtime below the application layer with no source-level or on-disk trace, defeating the reproducible-build defense that catches Thompson. Stuxnet, dr0wned, and FLAW3D are mapped at the same second order in different substrates, showing the taxonomy holds across software and physical manufacturing pipelines. The classification yields concrete defenses (hardware-rooted driver attestation, diverse independent toolchains across validation hosts, air-gapped reference baselines) that follow from the framework rather than the incident report.Companion to Bilar (2026), \"Volt Typhoon as Operational Realization of the nth-Order Bleeding Thesis\" (Zenodo 19739954)."}10{"work_id": 4410153295, "title": "Bursting Firings in Memristive Hopfield Neural Network With Image Encryption and Hardware Implementation", "abstract": "By integrating memristors into a Hopfield neural network (HNN), a diverse range of dynamical behavior can be generated, which has significant implications for modeling and biomimetic applications of artificial neurons. However, research on the firing dynamics of HNNs remains relatively limited. In response, a memristive tri-neurons Hopfield neural network (MTN-HNN) was constructed, with the synapse of the second neuron replaced by the proposed memristor. A theoretical and experimental investigation of the dynamics of this neural network was conducted using general analytical tools, such as phase diagrams, Lyapunov exponents, bifurcation diagrams, and others. Experimental results indicate that the dynamics of the MTN-HNN is influenced by the internal parameters of the memristor, enabling the network to extend attractors in up to two directions and thereby form grid multi-scrolls. Notably, the MTN-HNN exhibits various firing modes, including periodic and chaotic bursting. Finally, an encryption scheme was proposed to demonstrate the potential of the MTN-HNN, and both the custom digital circuits and the encryption scheme were successfully implemented on a Field-Programmable Gate Array (FPGA)."}11{"work_id": 6922095860, "title": "Practical Mechanisms for Reducing Processor\u2013Memory Data Movement in Modern Workloads", "abstract": "Data movement between the memory system and computation units is one of the most critical challenges in designing high performance and energy-efficient computing systems. The high cost of data movement is forcing architects to rethink the fundamental design of computer systems. Recent advances in memory design enable the opportunity for architects to avoid unnecessary datamovement by performing processing-in-memory (PIM), also known as near-data processing (NDP). While PIM can allow many data-intensive applications to avoid moving data from memory to the CPU, it introduces new challenges for system architects and programmers. Our goal in this thesisis to make PIM effective and practical in conventional computing systems. Toward this end, this thesis presents three major directions: (1) examining the suitability of PIM across key workloads, (2) addressing major system challenges for adopting PIM in computing systems, and (3) redesigning applications aware of PIM capability. In line with these three major directions, we propose a seriesof practical mechanisms to reduce processor\u2013memory data movement in modern workloads: First, we comprehensively analyze the energy and performance impact of data movement for several widely-used Google consumer workloads. We find that PIM can significantly reduce datamovement for all of these workloads, by performing part of the computation close to memory. Each workload contains simple primitives and functions that contribute to a significant amount of the overall data movement. We investigate whether these primitives and functions are feasible to implement using PIM, given the limited area and power constraints of consumer devices. Our analysis shows that offloading these primitives to PIM logic, consisting of either simple cores or specialized accelerators, eliminates a large amount of data movement, and significantly reducestotal system energy execution time. Second, we address one of the key system challenges for communication with PIM logic by proposing an efficient cache coherence support for near-data accelerators (NDAs). We find that enforcing coherence with the rest of the system, which is already a major challenge for on-chip accelerators, becomes more difficult for NDAs. This is because (1) the cost of communication between NDAs and CPUs is high, and (2) NDA applications generate a large amount of off-chip datamovement. As a result, as we show in this work, existing coherence mechanisms eliminate most of the benefits of NDAs. Based on our observations, we propose CoNDA, a coherence mechanism that lets an NDA optimistically execute an NDA kernel, under the assumption that the NDA has all necessary coherence permissions. This optimistic execution allows CoNDA to gather information on the memory accesses performed by the NDA and by the rest of the system. CoNDA exploits this information to avoid performing unnecessary coherence requests, and thus, significantly reducesdata movement for coherence. We show that CoNDA significantly improves performance andreduces energy consumption compared to prior coherence mechanisms. Third, we propose a hardware\u2013software co-design approach aware of PIM for edge machinelearning (ML) accelerators to enable energy-efficient and high-performance inference execution. We analyze a commercial Edge TPU (tensor processing unit) using 24 Google edge neural network (NN) models (including CNNs, LSTMs, transducers, and RCNNs), and find that the accelerator suffers from three shortcomings, in terms of computational throughput, energy efficiency, andmemory access handling. We comprehensively study the characteristics of each NN layer in all of the Google edge models, and find that these shortcomings arise from the one-size-fits-all approach of the accelerator, as there is a high amount of heterogeneity in key layer characteristics both across different models and across different layers in the same model. To combat this inefficiency, we propose a new acceleration framework called Mensa. Mensa incorporates multiple heterogeneous ML edge accelerators (including both on-chip and near-data accelerators), each of which caters to the characteristics of a particular subset of models. At runtime, Mensa schedules each layer torun on the best-suited accelerator, accounting for both efficiency and inter-layer dependencies. We show that Mensa significantly improves inference energy and throughput, while reducing hardware cost and improving area efficiency over the Edge TPU and Eyeriss v2, two state-of-the-art edge ML accelerators. Lastly, we propose to redesign emerging modern hybrid databases to be aware of PIM capability, to enable real-time analysis. Hybrid transactional and analytical processing (HTAP) databasesystems can support real-time data analysis without the high costs of synchronizing across separate single-purpose databases. Unfortunately, for many applications that perform a high rate of data updates, state-of-the-art HTAP systems incur significant drops in transactional and/or analyticalthroughput compared to performing only transactions or only analytics in isolation, due to (1) data movement between the CPU and memory, (2) data update propagation, and (3) consistency costs. We propose Polynesia, a hardware\u2013software co-designed system for in-memory HTAP databases. Polynesia (1) divides the HTAP system into transactional and analytical processing islands, (2) implements custom algorithms and hardware to reduce the costs of update propagation and consistency, and (3) exploits processing-in-memory for the analytical islands to alleviate data movement. We show that Polynesia significantly outperforms three state-of-the-art HTAP systems and reduces energy consumption."}12{"work_id": 4415057924, "title": "Electricity demand and grid impacts of AI data centers: Challenges and prospects", "abstract": "The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. Understanding the characteristics of AI data center loads and their interactions with the grid is therefore critical for ensuring both reliable power system operation and sustainable AI development. This paper provides a comprehensive review and vision of this evolving landscape. Specifically, this paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key characteristics and patterns of electricity demand across the stages of model preparation, training, fine-tuning, and inference, (iii) analyzes the critical challenges that AI data center loads pose to power systems across three interrelated timescales, including long-term planning and interconnection, short-term operation and electricity markets, and real-time dynamics and stability, and (iv) discusses potential solutions from the perspectives of the grid, AI data centers, and AI end-users to address these challenges. By synthesizing current knowledge and outlining future directions, this review aims to guide research and development in support of the joint advancement of AI data centers and power systems toward reliable, efficient, and sustainable operation."}13{"work_id": 7131231908, "title": "Reconfigurable Multiscroll Memristive Neural Network With Application to Telemedicine Privacy Protection", "abstract": "Constructing memristive neural networks (MNNs) with multiscroll chaotic attractors helps advance both theoretical and applied research on neural networks. However, the existing models mainly utilize complex memristor models with polynomial functions, nested composite functions, and so on, to generate multiscroll chaotic attractors, which leads to increased model complexity and difficulties in on-demand adjustment. Hence, this article proposes a reconfigurable multiscroll MNN (RMMNN) that can yield different types of multiscroll chaotic attractors merely by altering the memristive parameters without modifying its model. Through numerical methods, the complex dynamics of the RMMNN in different cases are analyzed, such as parameter-controlled multiscroll chaotic attractors, adjustable multistability, and parameter-induced transitions of multistability. In addition, the reliability of the numerical analysis is verified via the hardware circuits. Moreover, to address the issues of image security and low quality in telemedicine, a bidirectional rotation medical image encryption scheme (BRMIES) is developed based on the good pseudorandom chaotic sequences generated by RMMNN. Performance analysis demonstrates that BRMIES can effectively protect medical image and robustly handle various potential adverse interferences within telemedicine process."}14{"work_id": 4407217670, "title": "vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention", "abstract": "PagedAttention is a popular approach for dynamic memory allocation in LLM serving systems. It enables on-demand allocation of GPU memory to mitigate KV cache fragmentation - a phenomenon that crippled the batch size (and consequently throughput) in prior systems. However, in trying to allocate physical memory at runtime, PagedAttention ends up changing the virtual memory layout of the KV cache from contiguous to non-contiguous. Such a design leads to non-trivial programming and performance overheads."}15{"work_id": 4407091026, "title": "Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study", "abstract": "BACKGROUND: Emerging evidence suggests that artificial intelligence (AI) can increase cancer detection in mammography screening while reducing screen-reading workload, but further understanding of the clinical impact is needed. METHODS: In this randomised, controlled, parallel-group, non-inferiority, single-blinded, screening-accuracy study, done within the Swedish national screening programme, women recruited at four screening sites in southwest Sweden (Malm\u00f6, Lund, Landskrona, and Trelleborg) who were eligible for mammography screening were randomly allocated (1:1) to AI-supported screening or standard double reading. The AI system (Transpara version 1.7.0 ScreenPoint Medical, Nijmegen, Netherlands) was used to triage screening examinations to single or double reading and as detection support highlighting suspicious findings. This is a protocol-defined analysis of the secondary outcome measures of recall, cancer detection, false-positive rates, positive predictive value of recall, type and stage of cancer detected, and screen-reading workload. This trial is registered at ClinicalTrials.gov, NCT04838756 and is closed to accrual. FINDINGS: Between April 12, 2021, and Dec 7, 2022, 105 934 women were randomly assigned to the intervention or control group. 19 women were excluded from the analysis. The median age was 53\u00b77 years (IQR 46\u00b75-63\u00b72). AI-supported screening among 53 043 participants resulted in 338 detected cancers and 1110 recalls. Standard screening among 52 872 participants resulted in 262 detected cancers and 1027 recalls. Cancer-detection rates were 6\u00b74 per 1000 (95% CI 5\u00b77-7\u00b71) screened participants in the intervention group and 5\u00b70 per 1000 (4\u00b74-5\u00b76) in the control group, a ratio of 1\u00b729 (95% CI 1\u00b709-1\u00b751; p=0\u00b70021). AI-supported screening resulted in an increased detection of invasive cancers (270 vs 217, a proportion ratio of 1\u00b724 [95% CI 1\u00b704-1\u00b748]), wich were mainly small lymph-node negative cancers (58 more T1, 46 more lymph-node negative, and 21 more non-luminal A). AI-supported screening also resulted in an increased detection of in situ cancers (68 vs 45, a proportion ratio of 1\u00b751 [1\u00b703-2\u00b719]), with about half of the increased detection being high-grade in situ cancer (12 more nuclear grade III, and no increase in nuclear grade I). The recall and false-positive rate were not significantly higher in the intervention group (a ratio of 1\u00b708 [95% CI 0\u00b799-1\u00b717; p=0\u00b7084] and 1\u00b701 [0\u00b791-1\u00b711; p=0\u00b792], respectively). The positive predictive value of recall was significantly higher in the intervention group compared with the control group, with a ratio of 1\u00b719 (95% CI 1\u00b704-1\u00b737; p=0\u00b7012). There were 61 248 screen readings in the intervention group and 109 692 in the control group, resulting in a 44\u00b72% reduction in the screen-reading workload. INTERPRETATION: The findings suggest that AI contributes to the early detection of clinically relevant breast cancer and reduces screen-reading workload without increasing false positives. FUNDING: Swedish Cancer Society, Confederation of Regional Cancer Centres, and Swedish governmental funding for clinical research."}16{"work_id": 3107463944, "title": "Quantum computational advantage using photons", "abstract": "A light approach to quantum advantage Quantum computational advantage or supremacy is a long-anticipated milestone toward practical quantum computers. Recent work claimed to have reached this point, but subsequent work managed to speed up the classical simulation and pointed toward a sample size\u2013dependent loophole. Quantum computational advantage, rather than being a one-shot experimental proof, will be the result of a long-term competition between quantum devices and classical simulation. Zhong et al. sent 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer and sampled the output using 100 high-efficiency single-photon detectors. By obtaining up to 76-photon coincidence, yielding a state space dimension of about 10 30 , they measured a sampling rate that is about 10 14 -fold faster than using state-of-the-art classical simulation strategies and supercomputers. Science , this issue p. 1460"}17{"work_id": 7131624127, "title": "Zentropy-Enhanced Multigranularity Knowledge Modeling for Robust Feature Selection", "abstract": "Multigranularity knowledge modeling is an influential study for information processing and knowledge discovery in artificial intelligence (AI). A central research focus is the multigranularity representation and learning of knowledge structures. Among them, fuzzy rough sets (FRSs) have emerged as a representative method for characterizing uncertain knowledge. However, the existing FRS studies still exhibit two limitations: low robustness in knowledge acquisition and incomplete characterization of uncertainty. Hence, this article proposes a zentropy-enhanced multigranularity knowledge modeling framework for robust feature selection (ZeMG-FS). Specifically, we design a fast and adaptive multigranularity information granulation mechanism based on generalized granular-ball generation to effectively capture data distributions embedded in complex data. Then, the fuzzy rough approximation method is incorporated into the representation of multigranularity knowledge. Furthermore, we analyze the fundamental relationships and structures of the multigranularity knowledge model to introduce a novel multilevel zentropy. Unlike existing entropy measures, the primary consideration of the proposed zentropy is to match and enhance the performance of the proposed model. Finally, we design two feature evaluation criteria grounded in the model and apply them to feature selection. Extensive experiments demonstrate that our proposed methods achieve superior robustness and effectiveness compared with state-of-the-art approaches."}18{"work_id": 4401211704, "title": "Splitwise: Efficient Generative LLM Inference Using Phase Splitting", "abstract": "Generative large language model (LLM) applications are growing rapidly, leading to large-scale deployments of expensive and power-hungry GPUs. Our characterization of LLM inference shows that each inference request undergoes two phases: a compute-intensive prompt computation phase and a memory intensive token generation phase, each with distinct latency, throughput, memory, and power characteristics. Despite state-of-the-art batching and scheduling, the token generation phase underutilizes compute resources. Unlike prompt computation, token generation does not need the compute capability of the latest GPUs and can be run with lower power and cost. Based on these insights, we propose Splitwise, a model deployment and scheduling technique that splits the two phases of LLM inference requests on to separate machines. Splitwise enables phase-specific resource management using hardware that is well suited for each phase. Request state is transferred efficiently between machines using optimized network libraries on the fast back-plane interconnects available in today\u2019s GPU clusters. Using Splitwise, we design homogeneous and heterogeneous LLM inference clusters optimized for throughput, cost, and power Compared to current designs, Splitwise clusters achieve up to $1.4 \\times$ higher throughput at $\\mathbf{2 0 \\%}$ lower cost. Alternatively, they can deliver $2.35 \\times$ more throughput under the same power and cost budgets."}19{"work_id": 3010663313, "title": "FlatStore: An Efficient Log-Structured Key-Value Storage Engine for Persistent Memory", "abstract": "Emerging hardware like persistent memory (PM) and high-speed NICs are promising to build efficient key-value stores. However, we observe that the small-sized access pattern in key-value stores doesn't match with the persistence granularity in PMs, leaving the PM bandwidth underutilized. This paper proposes an efficient PM-based key-value storage engine named FlatStore. Specifically, it decouples the role of a KV store into a persistent log structure for efficient storage and a volatile index for fast indexing. Upon it, FlatStore further incorporates two techniques: 1) compacted log format to maximize the batching opportunity in the log; 2) pipelined horizontal batching to steal log entries from other cores when creating a batch, thus delivering low-latency and high-throughput performance. We implement FlatStore with the volatile index of both a hash table and Masstree. We deploy FlatStore on Optane DC Persistent Memory, and our experiments show that FlatStore achieves up to 35 Mops/s with a single server node, 2.5 - 6.3 times faster than existing systems."}20{"work_id": 3138262672, "title": "SimuBoost: Scalable Parallelization of Functional System Simulation", "abstract": "F\u00fcr das Sammeln detaillierter Laufzeitinformationen, wie Speicherzugriffsmustern, wird in der Betriebssystem- und Sicherheitsforschung h\u00e4ufig auf die funktionale Systemsimulation zur\u00fcckgegriffen. Der Simulator f\u00fchrt dabei die zu untersuchende Arbeitslast in einer virtuellen Maschine (VM) aus, indem er schrittweise Instruktionen interpretiert oder derart \u00fcbersetzt, sodass diese auf dem Zustand der VM arbeiten. Dieser Prozess erm\u00f6glicht es, eine umfangreiche Instrumentierung durchzuf\u00fchren und so an Informationen zum Laufzeitverhalten zu gelangen, die auf einer physischen Maschine nicht zug\u00e4nglich sind. Obwohl die funktionale Systemsimulation als m\u00e4chtiges Werkzeug gilt, stellt die durch die Interpretation oder \u00dcbersetzung resultierende immense Ausf\u00fchrungsverlangsamung eine substanzielle Einschr\u00e4nkung des Verfahrens dar. Im Vergleich zu einer nativen Ausf\u00fchrung messen wir f\u00fcr QEMU eine 30-fache Verlangsamung, wobei die Aufzeichnung von Speicherzugriffen diesen Faktor verdoppelt. Mit Simulatoren, die umfangreichere Instrumentierungsm\u00f6glichkeiten mitbringen als QEMU, kann die Verlangsamung um eine Gr\u00f6\u00dfenordnung h\u00f6her ausfallen. Dies macht die funktionale Simulation f\u00fcr lang laufende, vernetzte oder interaktive Arbeitslasten uninteressant. Dar\u00fcber hinaus erzeugt die Verlangsamung ein unrealistisches Zeitverhalten, sobald Aktivit\u00e4ten au\u00dferhalb der VM (z. B. Ein-/Ausgabe) involviert sind. In dieser Arbeit stellen wir SimuBoost vor, eine Methode zur drastischen Beschleunigung funktionaler Systemsimulation. SimuBoost f\u00fchrt die zu untersuchende Arbeitslast zun\u00e4chst in einer schnellen hardwaregest\u00fctzten virtuellen Maschine aus. Dies erm\u00f6glicht volle Interaktivit\u00e4t mit Benutzern und Netzwerkger\u00e4ten. W\u00e4hrend der Ausf\u00fchrung erstellt SimuBoost periodisch Abbilder der VM (engl. Checkpoints). Diese dienen als Ausgangspunkt f\u00fcr eine parallele Simulation, bei der jedes Intervall unabh\u00e4ngig simuliert und analysiert wird. Eine heterogene deterministische Wiederholung (engl. heterogeneous deterministic Replay) garantiert, dass in dieser Phase die vorherige hardwaregest\u00fctzte Ausf\u00fchrung jedes Intervalls exakt reproduziert wird, einschlie\u00dflich Interaktionen und realistischem Zeitverhalten. Unser Prototyp ist in der Lage, die Laufzeit einer funktionalen Systemsimulation deutlich zu reduzieren. W\u00e4hrend mit herk\u00f6mmlichen Verfahren f\u00fcr die Simulation des Bauprozesses eines modernen Linux \u00fcber 5 Stunden ben\u00f6tigt werden, schlie\u00dft SimuBoost die Simulation in nur 15 Minuten ab. Dies sind lediglich 16% mehr Zeit, als der Bau in einer schnellen hardwaregest\u00fctzten VM in Anspruch nimmt. SimuBoost ist imstande, diese Geschwindigkeit auch bei voller Instrumentierung zur Aufzeichnung von Speicherzugriffen beizubehalten. Die vorliegende Arbeit ist das erste Projekt, welches das Konzept der Partitionierung und Parallelisierung der Ausf\u00fchrungszeit auf die interaktive Systemvirtualisierung in einer Weise anwendet, die eine sofortige parallele funktionale Simulation gestattet. Wir erg\u00e4nzen die praktische Umsetzung mit einem mathematischen Modell zur formalen Beschreibung der Beschleunigungseigenschaften. Dies erlaubt es, f\u00fcr ein gegebenes Szenario die voraussichtliche parallele Simulationszeit zu prognostizieren und gibt eine Orientierung zur Wahl der optimalen Intervalll\u00e4nge. Im Gegensatz zu bisherigen Arbeiten legt SimuBoost einen starken Fokus auf die Skalierbarkeit \u00fcber die Grenzen eines einzelnen physischen Systems hinaus. Ein zentraler Schl\u00fcssel hierzu ist der Einsatz moderner Checkpointing-Technologien. Im Rahmen dieser Arbeit pr\u00e4sentieren wir zwei neuartige Methoden zur effizienten und effektiven Kompression von periodischen Systemabbildern."}21{"work_id": 7129692983, "title": "Spatio-Temporal Coordinated Operation Strategy of Data Centers Considering Virtual Storage System via Two-Stage Distributionally Robust Optimization", "abstract": "Energy consumption in data centers is growing rapidly in recent years. To realize the economic and flexible operation of data centers, this paper proposes a spatio-temporal coordinated operation strategy of data centers considering virtual storage system (VSS) via two-stage distributionally robust optimization. First, based on the dynamic thermal network of buildings and differentiated demands of cloud users in data centers, a comprehensive operation model with VSS is proposed. Then, considering uncertainties of general load demands from data centers and outdoor temperatures, a fuzzy set is deduced, using an imprecise Dirichlet model derived from historical data. This ensures confidence level satisfaction for uncertain sets of general load and outdoor temperatures. In addition, the spatio-temporal coordinated operation strategy of data centers considering virtual storage system via two-stage distributionally robust optimization that flexibly adjusts conservativeness through uncertainty regulation parameters. Finally, a Dynamic Accuracy Column and Constraint Generation (DA-C&CG) algorithm is developed for solving the proposed strategy. The results indicate that the proposed strategy can effectively enhance operational economics, computing efficiency and resilience by leveraging flexibility in cooling and cloud user loads within suitable server temperatures. Meanwhile, the DA-C&CG algorithm exhibits excellent solution performance."}22{"work_id": 4294786246, "title": "Time-optimal holonomic quantum computation", "abstract": "A three-level system can be used in a \u039b-type configuration in order to construct auniversal set of non-adiabatic quantum gates through the use of non-Abelian non-adiabatic geometrical phases. Such construction allows for high-speed operation times which diminish the effects of decoherence. This might be, however, accompanied by a breakdown of the validity of the rotating wave approximation (RWA) due to the comparable timescale between the counter-rotating terms and the pulse length, which greatly affects the dynamics. Here we investigate the trade-off between dissipative effects and the RWA validity, obtaining the optimal regime for the operation of the holonomic quantum gates."}23{"work_id": 4394944658, "title": "Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications", "abstract": "GPUs are critical for maximizing the throughput-per-Watt of deep neural network (DNN) applications. However, DNN applications often underutilize GPUs, even when using large batch sizes and eliminating input data processing or communication stalls. DNN workloads consist of data-dependent operators, with different compute and memory requirements. While an operator may saturate GPU compute units or memory bandwidth, it often leaves other GPU resources idle. Despite the prevalence of GPU sharing techniques, current approaches are not sufficiently fine-grained or interference-aware to maximize GPU utilization while minimizing interference at the granularity of 10s of \u03bcs. We propose Orion, a system that transparently intercepts GPU kernel launches from multiple clients sharing a GPU. Orion schedules work on the GPU at the granularity of individual operators and minimizes interference by taking into account each operator's compute and memory requirements. We integrate Orion in PyTorch and demonstrate its benefits in various DNN workload collocation use cases. Orion significantly improves tail latency compared to state-of-the-art baselines for a high-priority inference job while collocating best-effort inference jobs to increase per-GPU request throughput by up to 7.3\u00d7, or while collocating DNN training, saving up to 1.49\u00d7 in training costs compared to dedicated GPU allocation."}24{"work_id": 3021475380, "title": "Keystone", "abstract": "Trusted execution environments (TEEs) see rising use in devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current vendor-specific TEEs makes a fixed set of trade-offs with little room for customization. We present Keystone---the first open-source framework for building customized TEEs. Keystone uses simple abstractions provided by the hardware such as memory isolation and a programmable layer underneath untrusted components (e.g., OS). We build reusable TEE core primitives from these abstractions while allowing platform-specific modifications and flexible feature choices. We showcase how Keystone-based TEEs run on unmodified RISC-V hardware and demonstrate the strengths of our design in terms of security, TCB size, execution of a range of benchmarks, applications, kernels, and deployment models."}25{"work_id": 3014555390, "title": "CacheFlow: Using Maximum Flow to Bound Cache-Based Preemption Delays", "abstract": "Cache-related preemption delay (CRPD) analysis bounds the additional execution time caused by cache evictions during preemptions. Tightly bounding CRPDs is challenging as there are many possible preemption patterns that can occur at runtime, and thus there has been continuous work over three decades to refine these bounds. This paper presents CacheFlow, a framework that formulates total CRPD as a maximum-flow problem. In the flow network, nodes and edge capacities can be constructed to model certain eviction patterns that can occur. Therefore, by (safely) removing nodes or edges, or reducing edge capacities, tighter CRPD bounds can be derived. This is demonstrated with different CacheFlow refinements, some of which include insights from prior analyses, as well as a refinement for simply periodic systems. An iterative max-flow formulation is also described to more efficiently integrate the max-flow solving in the context of standard fixed-priority response-time analysis. Experiments on synthetic task systems demonstrate significant schedulability improvements across a range of system configurations, while also having reasonable solving times."}26{"work_id": 3012028616, "title": "Catalyzer", "abstract": "Serverless computing promises cost-efficiency and elasticity for high-productive software development. To achieve this, the serverless sandbox system must address two challenges: strong isolation between function instances, and low startup latency to ensure user experience. While strong isolation can be provided by virtualization-based sandboxes, the initialization of sandbox and application causes non-negligible startup overhead. Conventional sandbox systems fall short in low-latency startup due to their application-agnostic nature: they can only reduce the latency of sandbox initialization through hypervisor and guest kernel customization, which is inadequate and does not mitigate the majority of startup overhead."}27{"work_id": 4295682935, "title": "CodaLab Competitions An Open Source Platform to Organize Scientific Challenges", "abstract": "CodaLab Competitions is an open source web platform designed to help data scientists and research teams to crowd-source the resolution of machine learning problems through the organization of competitions, also called challenges or contests. CodaLab Competitions provides useful features such as multiple phases, results and code submissions, multi-score leaderboards, and jobs running inside Docker containers. The platform is very flexible and can handle large scale experiments, by allowing organizers to upload large datasets and provide their own CPU or GPU compute workers."}28{"work_id": 1868882999, "title": "Formal verification techniques using quantum process calculus", "abstract": "Quantum communication is a rapidly growing area of research and development. While the successful construction of a large-scale quantum computer may be some years away, there are already commercial implementations of secure communication using quantum cryptography. The application of formal methods to classical communication and cryptographic systems has been very successful, and is now widely used in industry by organisations such as Intel, Microsoft and NASA. There is reason to believe that similar benefits can be expected for the verification of quantum systems. In this thesis, we focus on the use of process calculus, specifically Communicating Quantum Processes (CQP), for the analysis of quantum protocols. Congruence relations are an important aspect of process calculus, since they provide the foundation for equational reasoning. Previous work on congruence relations for quantum processes excluded the classical information arising from measurements, and was therefore unable to analyse many of the interesting known quantum communication protocols. Developing a congruence relation for general quantum processes is difficult because of the interaction between measurement, entanglement and parallel composition. We define a labelled transition relation for CQP in order to describe external interactions. Based on this semantics, we define a notion of observational equivalence for CQP processes, namely probabilistic branching bisimilarity. We find that this relation is not preserved by parallel composition, however we are able to gain a deeper understanding of the link between probabilistic branching and measurement. Based on this newfound understanding, we present a novel semantics for quantum processes, combining mixed quantum states with probabilistic branching. With respect to this new semantic model, we define full probabilistic branching bisimilarity and prove that it is a congruence. We use this congruence relation to discuss an axiomatic approach to the verification of quantum processes. The quantum teleportation protocol is used as a primary example throughout, and we prove that it is congruent to a quantum channel. We define a translation from CQP to the Quantum Model Checker (QMC) in order to provide automated verification techniques using CQP specifications. We prove that this translation preserves the semantics of CQP processes, thereby enabling a multifaceted approach to formal verification by enhancing the manual techniques of process calculus with the benefits of model checking."}29{"work_id": 4393145114, "title": "AI and Memory Wall", "abstract": "The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck is increasingly shifting to memory bandwidth. Over the past 20 years, peak server hardware FLOPS has been scaling at 3.0\u00d7/2yrs, outpacing the growth of DRAM and interconnect bandwidth, which have only scaled at 1.6 and 1.4 times every 2 years, respectively. This disparity has made memory, rather than compute, the primary bottleneck in AI applications, particularly in serving. Here, we analyze encoder and decoder Transformer models and show how memory bandwidth can become the dominant bottleneck for decoder models. We argue for a redesign in model architecture, training, and deployment strategies to overcome this memory limitation."}30{"work_id": 3023238978, "title": "Autopilot", "abstract": "In many public and private Cloud systems, users need to specify a limit for the amount of resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits might be throttled or killed, resulting in delaying or dropping end-user requests, so human operators naturally err on the side of caution and request a larger limit than the job needs. At scale, this results in massive aggregate resource wastage."}31{"work_id": 7160873310, "title": "Enhancing the Performance Prediction of Quantum Computing Algorithms using Gradient Boosting and Ada Boost Regression", "abstract": "Quantum computing is considered to have tremendous potential to help take the emerging field of \"Computational Law\" to the next level of growth in terms of the expression and implementation of legal principles. With the promise of quantum technology's increasing influence on the legal industry in mind, this essay utilizes the emerging field of Computational Complexity Theory to explore the types of problems that quantum computing is capable of solving more efficiently than classical computing, which is referred to as Quantum Supremacy. From this foundation, three emerging areas within the legal sector have been identified where quantum computing is likely to show transformative superiority. While it is unknown how quantum technology will ultimately integrate into the legal world, this essay argues that its use in the future is contingent upon the establishment of a creative vision of possibilities Key words: Quantum Computing, Quantum Mechanics, Qubits, Data Analytics, Computational Intelligence, Superposition, Entanglement, Quantum Algorithms, Quantum Supremacy, Quantum Cryptography."}32{"work_id": 4287393709, "title": "Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions", "abstract": "Predictable timing on multicore systems requires careful management of shared resources such as the last-level cache and memory bandwidth. This paper presents MPORA, an uncertainty-aware dynamic resource allocation framework for multi-path, input-dependent real-time tasks on multicore platforms. MPORA models each job as a discrete-time dynamical system that captures execution dynamics and resource-dependent performance indicators. At runtime, MPORA monitors job execution states and predicts short-term instruction rates and remaining execution times under candidate allocations using predictive models trained offline. It then solves a receding-horizon optimization problem to compute resource allocations that maximize system-wide progress while meeting job deadlines. To address prediction uncertainty, MPORA integrates weighted conformal prediction into the optimization formulation, enabling uncertainty-aware deadline constraints. We implement MPORA as a Linux kernel module with microsecond-scale inference overhead. Experimental results on SPEC CPU benchmarks show that MPORA delivers accurate predictions under unseen inputs and distribution shifts with low overhead, while improving schedulability and response times over existing methods."}33{"work_id": 4417057079, "title": "Single-shot matrix-matrix photonic processor based on spatial-spectral hypermultiplexed parallel diffraction", "abstract": "Abstract The ever-increasing data demand craves advancements in high-speed and energy-efficient computing hardware. Analog optical neural network (ONN) processors have emerged as a promising solution, offering benefits in bandwidth and energy consumption. However, existing ONN processors exhibit limited computational parallelism, and while certain architectures achieve high parallelism, they encounter serious scaling roadblocks for large-scale implementation. This restricts the throughput, latency, and energy efficiency advantages of ONN processors. Here, we introduce a spatial-wavelength-temporal hyper-multiplexed ONN processor that supports high data dimensionality, high computing parallelism and is feasible for large-scale implementation, and in a single time step, a three-dimensional matrix-matrix multiplication (MMM) optical tensor processor is demonstrated. Our hardware accelerates convolutional neural networks (CNNs) and deep neural networks (DNNs) through parallel matrix multiplication. We demonstrate benchmark image recognition using a CNN and a subsequently fully connected DNN in the optical domain. The network works with 292,616 weight parameters under ultra-low optical energy of \u2248 20 attojoules (aJ) per multiply and accumulate (MAC) at 96.4% classification accuracy. The system supports broad spectral and spatial bandwidths and is capable for large-scale demonstration, paving the way for highly efficient large-scale optical computing for next-generation deep learning."}34{"work_id": 4321610982, "title": "Suppressing quantum errors by scaling a surface code logical qubit", "abstract": "Abstract Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction 1,2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 \u00b1 0.016)% compared to (3.028 \u00b1 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 \u00d7 10 \u22126 logical error per cycle floor set by a single high-energy event (1.6 \u00d7 10 \u22127 excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation."}35{"work_id": 3156891177, "title": "Carbon Emissions and Large Neural Network Training", "abstract": "The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact and finding greener strategies, yet it is challenging without detailed information. We calculate the energy use and carbon footprint of several recent large models-T5, Meena, GShard, Switch Transformer, and GPT-3-and refine earlier estimates for the neural architecture search that found Evolved Transformer. We highlight the following opportunities to improve energy efficiency and CO2 equivalent emissions (CO2e): Large but sparsely activated DNNs can consume <1/10th the energy of large, dense DNNs without sacrificing accuracy despite using as many or even more parameters. Geographic location matters for ML workload scheduling since the fraction of carbon-free energy and resulting CO2e vary ~5X-10X, even within the same country and the same organization. We are now optimizing where and when large models are trained. Specific datacenter infrastructure matters, as Cloud datacenters can be ~1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators inside them can be ~2-5X more effective than off-the-shelf systems. Remarkably, the choice of DNN, datacenter, and processor can reduce the carbon footprint up to ~100-1000X. These large factors also make retroactive estimates of energy cost difficult. To avoid miscalculations, we believe ML papers requiring large computational resources should make energy consumption and CO2e explicit when practical. We are working to be more transparent about energy use and CO2e in our future research. To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models, and we are collaborating with MLPerf developers to include energy usage during training and inference in this industry standard benchmark."}36{"work_id": 4384697207, "title": "Methods and Techniques for Inter-cloud Data Transfers", "abstract": "This thesis focuses on methods and techniques for transferring data from a compute cloud to a storage cloud by using a buffer at the compute cloud.A space driven, a time driven, and a hybrid buffering technique are discussed.It was found that using buffering at the compute cloud is only beneficial when the load on the system is high.To improve performance a threshold-based and a machine learning based adaptive method are introduced at the buffer to dynamically change between buffering techniques at runtime based on the load on the system state.Using the adaptive methods leads to a significant improvement in performance.A proof-of-concept prototype was built for the different data transfer methods and techniques researched.Experiments performed on the prototype provide insights into system behaviour and performance."}37{"work_id": 2783525259, "title": "Loihi: A Neuromorphic Manycore Processor with On-Chip Learning", "abstract": "Loihi is a 60-mm2 chip fabricated in Intels 14-nm process that advances the state-of-the-art modeling of spiking neural networks in silicon. It integrates a wide range of novel features for the field, such as hierarchical connectivity, dendritic compartments, synaptic delays, and, most importantly, programmable synaptic learning rules. Running a spiking convolutional form of the Locally Competitive Algorithm, Loihi can solve LASSO optimization problems with over three orders of magnitude superior energy-delay-product compared to conventional solvers running on a CPU iso-process/voltage/area. This provides an unambiguous example of spike-based computation, outperforming all known conventional solutions."}38{"work_id": 2187800381, "title": "Practical lock-freedom", "abstract": "Mutual-exclusion locks are currently the most popular mechanism for interprocess synchronisation, largely due to their apparent simplicity and ease of implementation. In the parallel-computing environments that are increasingly commonplace in high-performance applications, this simplicity is deceptive: mutual exclusion does not scale well with large numbers of locks and many concurrent threads of execution. Highly-concurrent access to shared data demands a sophisticated \u2018fine-grained\u2019 locking strategy to avoid serialising non-conflicting operations. Such strategies are hard to design correctly and with good performance because they can harbour problems such as deadlock, priority inversion and convoying. Lock manipulations may also degrade the performance of cache-coherent multiprocessor systems by causing coherency conflicts and increased interconnect traffic, even when the lock protects read-only data. In looking for solutions to these problems, interest has developed in lock-free data structures. By eschewing mutual exclusion it is hoped that more efficient and robust systems can be built. Unfortunately the current reality is that most lock-free algorithms are complex, slow and impractical. In this dissertation I address these concerns by introducing and evaluating practical abstractions and data structures that facilitate the development of large-scale lock-free systems. Firstly, I present an implementation of two useful abstractions that make it easier to develop arbitrary lock-free data structures. Although these abstractions have been described in previous work, my designs are the first that can be practically implemented on current multiprocessor systems. Secondly, I present a suite of novel lock-free search structures. This is interesting not only because of the fundamental importance of searching in computer science and its wide use in real systems, but also because it demonstrates the implementation issues that arise when using the practical abstractions I have developed. Finally, I evaluate each of my designs and compare them with existing lock-based and lock-free alternatives. To ensure the strongest possible competition, several of the lock-based alternatives are significant improvements on the best-known solutions in the literature. These results demonstrate that it is possible to build useful data structures with all the perceived benefits of lock-freedom and with performance better than sophisticated lock-based designs. Furthermore, and contrary to popular belief, this work shows that existing hardware primitives are sufficient to build practical lock-free implementations of complex data structures."}39{"work_id": 7127097285, "title": "Nonlinear photonic neuromorphic chips for spiking reinforcement learning", "abstract": "Photonic computing chips have made significant progress in accelerating linear computations, but nonlinear computations are usually implemented in the digital domain, which introduces additional system latency and power consumption, and hinders the implementation of fully functional photonic neural network chips. Here, we propose and fabricate a 16-channel programmable incoherent photonic neuromorphic computing chip by co-designing a simplified Mach\u2013Zehnder interferometer (MZI) mesh and distributed feedback lasers with saturable absorber (DFBs-SA) array using different materials, enabling implementation of both linear and nonlinear spike computations in the optical domain through two separate chips. Furthermore, previous studies mainly focused on supervised learning and simple image classification tasks. Here, we propose a photonic spiking reinforcement learning (RL) architecture for the first, to our knowledge, time, and develop a software\u2013hardware collaborative training-inference framework ( in situ photonic training and hardware-aware fine-tuning) to address the challenge of training spiking RL models. We achieve large-scale, energy-efficient (photonic linear computation: 1.39 TOPS/W, photonic nonlinear computation: 987.65 GOPS/W), and low-latency (on-chip 320 ps) deployment of an entire layer of photonic spiking RL. Two RL benchmarks including the discrete CartPole task and the continuous Pendulum task are demonstrated experimentally based on the spiking proximal policy optimization (PPO) algorithm. The hardware\u2013software collaborative computing reward value converges to 200 (\u2212250) for the CartPole (Pendulum) tasks, respectively, comparable to that of a traditional PPO algorithm. This experimental demonstration addresses the challenge of the absence of large-scale on-chip photonic nonlinear spike computation and spiking RL training difficulty, and presents a high-speed and low-latency photonic spiking RL solution with promising application prospects in fields such as robot control, autonomous driving, and embodied intelligence."}40{"work_id": 7115707641, "title": "The carbon and water footprints of data centers and what this could mean for artificial intelligence", "abstract": "Although there are ways to estimate the global power demand of artificial intelligence (AI) systems, it remains challenging to quantify the associated carbon and water footprints. The lack of distinction between AI and non-AI workloads in the environmental reports of data center operators makes it possible to assess the environmental impact of AI workloads only by approximating them through data centers' general performance metrics. The environmental disclosure of tech companies is, however, often insufficient to determine even the total data center performance of these companies. The shortcomings in the environmental disclosure of data center operators could be remedied with new policies mandating the disclosure of additional metrics. Because the environmental impact of data centers is growing rapidly, the urgency of transparency in the tech sector is also increasing. The carbon footprint of AI systems alone could be between 32.6 and 79.7 million tons of CO 2 emissions in 2025, while the water footprint could reach 312.5\u2013764.6 billion L."}41{"work_id": 2963194964, "title": "L\u00e4mm\u00f6ns\u00e4\u00e4tely suprajohtavissa mikroaaltopiireiss\u00e4", "abstract": "Circuit quantum electrodynamics offers versatile opportunities for various discoveries infundamental physics, as well as for different applications such as sensitive detectors and,ultimately, even a large-scale quantum computer. Since these devices work at cryogenictemperatures and excess heat is a substantial source of errors, it is of great importance to controlthe heat. In this thesis, several techniques for heat control are experimentally investigated. We study heattransfer carried by photons between two heat baths, as well as heat flow between superconductingresonators and their environment. In particular, the focus is on using normal-metal componentsto enhance the operation of superconducting microwave circuits operating at the level of singleenergy quanta. First, we investigate long-distance single-channel heat conduction near the quantum limit, i.e.,the quantum of thermal conductance. As compared to earlier experiments, the distance is increasedby a factor of 10,000. In addition, we demonstrate emission and absorption of photons in amicrowave resonator by photon-assisted tunneling in normal-metal\u2013insulator\u2013superconductorjunctions. Another approach for absorbing photons is to use a normal-metal resistor in asuperconducting resonator with a tunable resonance frequency. Finally, we realize a circuitconsisting of two resonators with a singularity in the parameter space, known as an exceptionalpoint. In our circuit, the exceptional point enables the fastest possible heat transfer between thetwo resonators without back and forth oscillation. In the future, these methods may find applications in different cryogenic devices, including a fastinitialization of qubits. Furthermore, the methods may be utilized in the experimental studies offundamental physics, including further investigations of exceptional points in differentconfigurations."}42{"work_id": 4416596047, "title": "Toward Sustainable AI: A Scoping Review of Carbon Footprint and Environmental Impacts Across Training and Inference Stages", "abstract": "AI is spreading rapidly, creating significant social and economic value while also raising concerns about its high energy use and environmental sustainability. While prior studies have predominantly focused on the energy-intensive nature of the training phase, the cumulative environmental footprint generated during large-scale service operations, particularly in the inference phase, has received comparatively less attention and remains difficult to compare across studies due to inconsistent boundaries and disclosure practices. To bridge this gap, this study conducts a scoping review of methodologies and evidence on AI carbon footprint assessment. We analyze the classification and standardization status of existing AI carbon measurement tools and methodologies, and comparatively examine the environmental impacts arising from both training and inference stages, and synthesize inference evaluation into a benchmark-oriented framework. In addition, we identify how multidimensional factors such as model size, prompt complexity, serving environments, and system boundary definitions shape the resulting carbon footprint. Our review reveals critical limitations in current AI carbon accounting practices, including methodological inconsistencies, technology-specific biases, and insufficient attention to end-to-end system perspectives. Building on these insights, we propose future research and governance directions: (1) establishing standardized and transparent universal measurement protocols with clear boundary, functional-unit, and disclosure requirements, (2) designing dynamic evaluation frameworks that incorporate user behavior and service-level operating conditions, (3) developing life-cycle monitoring systems that encompass embodied emissions, and (4) advancing multidimensional sustainability assessment framework that balance model performance with environmental efficiency. This paper provides a foundation for interdisciplinary dialogue aimed at building a sustainable AI ecosystem and offers a baseline guideline for researchers and policymakers seeking to evaluate AI environmental impacts across technical, social, and operational dimensions."}43{"work_id": 2921153466, "title": "Basic Performance Measurements of the Intel Optane DC Persistent Memory Module", "abstract": "Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just \"Optane DC PMM\"). This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. This work comprises the first in-depth, scholarly, performance review of Intel's Optane DC PMM, exploring its capabilities as a main memory device, and as persistent, byte-addressable memory exposed to user-space applications. This report details the technologies performance under a number of modes and scenarios, and across a wide variety of macro-scale benchmarks. Optane DC PMMs can be used as large memory devices with a DRAM cache to hide their lower bandwidth and higher latency. When used in this Memory (or cached) mode, Optane DC memory has little impact on applications with small memory footprints. Applications with larger memory footprints may experience some slow-down relative to DRAM, but are now able to keep much more data in memory. When used under a file system, Optane DC PMMs can result in significant performance gains, especially when the file system is optimized to use the load/store interface of the Optane DC PMM and the application uses many small, persistent writes. For instance, using the NOVA-relaxed NVMM file system, we can improve the performance of Kyoto Cabinet by almost 2x. Optane DC PMMs can also enable user-space persistence where the application explicitly controls its writes into persistent Optane DC media. In our experiments, modified applications that used user-space Optane DC persistence generally outperformed their file system counterparts. For instance, the persistent version of RocksDB performed almost 2x faster than the equivalent program utilizing an NVMM-aware file system."}44{"work_id": 4283157527, "title": "Measuring the Carbon Intensity of AI in Cloud Instances", "abstract": "The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint. As a result, recent scholarship has called for better estimates of the greenhouse gas impact of AI: data scientists today do not have easy or reliable access to measurements of this information, which precludes development of actionable tactics. We argue that cloud providers presenting information about software carbon intensity to users is a fundamental stepping stone towards minimizing emissions."}45{"work_id": 2781738013, "title": "Quantum Computing in the NISQ era and beyond", "abstract": "Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away - we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing."}46{"work_id": 2792476970, "title": "Assessing Bootstrap", "abstract": "Bootstrap:Algebra is a curricular module designed to integrate introductory computing into an algebra class; the module aims to help students improve on various essential learning outcomes from state and national algebra standards. In prior work, we published initial findings about student performance gains on algebra problems after taking Bootstrap. While the results were promising, the dataset was not large, and had students working on algebra problems that had been scaffolded with Bootstrap's pedagogy. This paper reports on a more detailed study with (a) data from more than three times as many students, (b) analysis of performance changes in incorrect answers, (c) some problems in which the Bootstrap scaffolds have been removed, and (d) an IRT analysis across the elements of Bootstrap's program-design pedagogy. Our results confirm that students improve on algebraic word problems after completing the module, even on unscaffolded problems. The nature of incorrect answers to symbolic-form questions also appears to improve after Bootstrap."}47{"work_id": 4409126690, "title": "IBM quantum computers: evolution, performance, and future directions", "abstract": "Abstract Quantum computers represent a transformative frontier in computational technology, promising exponential speedups beyond classical computing limits. IBM Quantum has led significant advancements in both hardware and software, providing access to quantum hardware via IBM Cloud\u00ae since 2016 and achieving a milestone with the world\u2019s first accessible quantum computer. This paper explores IBM\u2019s journey in quantum computing, focusing on its contributions to both hardware and software, as well as the development of practical quantum computers. We trace the evolution of IBM Quantum\u2019s processors, from the early canary processors to the milestone of surpassing the 1000-qubit barrier. In addition to these technological strides, we delve into the practical applications of quantum computing, particularly within nine key industries: airlines, banking, healthcare, electronics, life sciences, and more. We also explore IBM Quantum\u2019s case studies and strategic partnerships with organizations such as Boeing, CERN, ExxonMobil, and Cleveland Clinic, which are helping to bridge the gap between theoretical research and real-world applications. Further, we examine the key challenges and solutions in scaling quantum systems and achieving fault tolerance, highlighting IBM\u2019s efforts toward building practical, fault-tolerant quantum systems capable of addressing real-world problems."}48{"work_id": 4406693713, "title": "Scaling and networking a modular photonic quantum computer", "abstract": "Photonics offers a promising platform for quantum computing1\u20134, owing to the availability of chip integration for mass-manufacturable modules, fibre optics for networking and room-temperature operation of most components. However, experimental demonstrations are needed of complete integrated systems comprising all basic functionalities for universal and fault-tolerant operation5. Here we construct a (sub-performant) scale model of a quantum computer using 35 photonic chips to demonstrate its functionality and feasibility. This combines all the primitive components as discrete, scalable rack-deployed modules networked over fibre-optic interconnects, including 84 squeezers6 and 36 photon-number-resolving detectors furnishing 12 physical qubit modes at each clock cycle. We use this machine, which we name Aurora, to synthesize a cluster state7 entangled across separate chips with 86.4 billion modes, and demonstrate its capability of implementing the foliated distance-2 repetition code with real-time decoding. The key building blocks needed for universality and fault tolerance are demonstrated: heralded synthesis of single-temporal-mode non-Gaussian resource states, real-time multiplexing actuated on photon-number-resolving detection, spatiotemporal cluster-state formation with fibre buffers, and adaptive measurements implemented using chip-integrated homodyne detectors with real-time single-clock-cycle feedforward. We also present a detailed analysis of our architecture\u2019s tolerances for optical loss, which is the dominant and most challenging hurdle to crossing the fault-tolerant threshold. This work lays out the path to cross the fault-tolerant threshold and scale photonic quantum computers to the point of addressing useful applications. A proof-of-principle study reports a complete photonic quantum computer architecture that can, once appropriate component performance is achieved, deliver a universal and fault-tolerant quantum computer."}49{"work_id": 4380627387, "title": "Evidence for the utility of quantum computing before fault tolerance", "abstract": "Abstract Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. However, the greatest impediment to realizing its full potential is noise that is inherent to these systems. The widely accepted solution to this challenge is the implementation of fault-tolerant quantum circuits, which is out of reach for current processors. Here we report experiments on a noisy 127-qubit processor and demonstrate the measurement of accurate expectation values for circuit volumes at a scale beyond brute-force classical computation. We argue that this represents evidence for the utility of quantum computing in a pre-fault-tolerant era. These experimental results are enabled by advances in the coherence and calibration of a superconducting processor at this scale and the ability to characterize 1 and controllably manipulate noise across such a large device. We establish the accuracy of the measured expectation values by comparing them with the output of exactly verifiable circuits. In the regime of strong entanglement, the quantum computer provides correct results for which leading classical approximations such as pure-state-based 1D (matrix product states, MPS) and 2D (isometric tensor network states, isoTNS) tensor network methods 2,3 break down. These experiments demonstrate a foundational tool for the realization of near-term quantum applications 4,5 ."}50{"work_id": 7140797778, "title": "Carbon Footprint Efficiency Ratio: A Unified Indicator for Green Evaluation of Evolutionary Algorithms", "abstract": "Evolutionary algorithms (EAs) are widely adopted for black-box optimization, yet evaluation protocols remain largely accuracy-centric and often omit computational energy use, hindering carbon-aware deployment in green computing settings. We propose the Carbon Footprint Efficiency Ratio (CFER) to quantify optimization benefit per unit carbon emission and enable a unified assessment of carbon efficiency. To compute CFER without dedicated power-monitoring hardware, we develop a device-independent energy estimation model based on the Multimodal Energy Consumption Factor (MECF). Using a dataset constructed from 13 benchmark functions and 8 classical EAs, MECF is identified via outlier removal and Huber regression, achieving a mean estimation error of 1.80% and a maximum error of 2.51%. On the CEC2020 test suite, CFER-based comparisons reveal rankings that differ substantially from accuracy-only evaluations, highlighting algorithms that improve carbon efficiency with only marginal accuracy loss. Beyond metric-based assessment, we introduce a CFER-aware green optimization mechanism as an algorithm-agnostic plug-in control layer. Driven by real-time carbon estimation and marginal CFER feedback, it adaptively budgets computation through carbon-budget termination and population shrinking under low marginal carbon efficiency. Integrated into GA, DE, and PSO for uncrewed aerial vehicle (UAV) three-dimensional path planning, the resulting CFER-aware variants reduce CO2emissions by 45.91%, 28.28%, and 59.14%, respectively, while increasing CFER by 78.59%, 32.45%, and 133.70%, respectively, yielding an improved performance\u2013carbon trade-off with minor performance-score degradation. Overall, the proposed framework supports carbon-aware evaluation, algorithm selection, and adaptive computation budgeting for green AI systems."}51{"work_id": 7130515337, "title": "Artificial intelligence literacy and academic resilience in undergraduate nursing students: the mediating role of self-efficacy and artificial intelligence anxiety", "abstract": "BACKGROUND: Artificial intelligence is increasingly integrated into nursing education, making AI literacy a key competency for students. Academic resilience is also essential for coping with learning challenges. However, the mechanisms linking AI literacy to academic resilience remain unclear, particularly the roles of self-efficacy and AI anxiety. This study examines these associations and tests the mediating effects of self-efficacy and AI anxiety. METHODS: A cross-sectional study was conducted among 415 undergraduate nursing students from a university in northern China. AI literacy, academic resilience, self-efficacy and AI anxiety were assessed using validated scales. Statistical analyses included descriptive analysis, Pearson correlation and serial mediation testing using the PROCESS macro in SPSS with bootstrap sampling. RESULTS: (1) Academic resilience was significantly and positively correlated with AI literacy (r = 0.229, p < 0.01) and self-efficacy (r = 0.340, p < 0.01), and negatively correlated with AI anxiety (r = -0.426, p < 0.01). AI literacy was positively correlated with self-efficacy (r = 0.118, p < 0.05) and negatively correlated with AI anxiety (r = -0.143, p < 0.01). (2) AI literacy was positively associated with self-efficacy (\u03b2 = 0.118, p = 0.017) and was negatively associated with AI anxiety (\u03b2 = -0.143, p = 0.006). Self-efficacy positively was positively associated with academic resilience (\u03b2 = 0.340, p < 0.001), while AI anxiety was negatively associated with academic resilience (\u03b2 = -0.426, p < 0.001). (3) In the model of AI literacy \u2192 self-efficacy \u2192 AI anxiety \u2192 academic resilience, the total indirect effect was 0.055. The mediating effect of self-efficacy accounted for 36.4% of the total indirect effect (0.020), while AI anxiety accounted for 58.2% (0.032). The serial mediation effect of self-efficacy and AI anxiety contributed 5.4% (0.003) to the total indirect effect, which was statistically significant but small in magnitude. Overall, the mediation pattern supported partial (rather than full) mediation. CONCLUSION: AI literacy is is an important correlate of nursing students\u2019 academic resilience. Strengthening AI literacy, improving self-efficacy and reducing AI anxiety may be linked to better adaptation to AI-supported learning environments. CLINICAL TRIAL NUMBER: Not applicable."}52{"work_id": 4407614581, "title": "Examining Teaching Competencies and Challenges While Integrating Artificial Intelligence in Higher Education", "abstract": "Abstract The rapid development of artificial intelligence (AI) technologies has demonstrated their affordances and limitations in revolutionizing pedagogical strategies in higher education. Given the lack of guidelines, policies, and resources to assist instructors in efficiently and ethically integrating AI into teaching and learning practices, this systematic review aimed to investigate AI integration competencies and challenges in higher education from the intelligent Technological Pedagogical Content Knowledge (TPACK) perspective. We first applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify 23 studies published between 2019 and 2023 that met the inclusion and exclusion criteria. After conducting open coding and thematic analysis, the research findings showed four AI integration competencies and strategies, including 1) AI literacy and readiness as intelligent technological knowledge, 2) AI-supported innovative pedagogy to supplement instructor social presence and transform the instructor-student relationship, 3) AI as intelligent learning partners to increase student engagement in self-regulated learning and higher-order thinking skills, and 4) AI-driven learning experience design and delivery. AI integration limitations and challenges contained: 1) the short-term novelty effect, 2) digital divides, 3) technical deficiencies, and 4) ethical concerns. Based on the implications for future practices, a diagram was developed to illustrate the systematic considerations to support the sustainability of AI-assisted teaching and learning in higher education."}53{"work_id": 4394998532, "title": "PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation", "abstract": "This paper introduces two extensions to the popular PyTorch machine learning framework, TorchDynamo and TorchInductor, which implement the torch.compile feature released in PyTorch 2. TorchDynamo is a Python-level just-in-time (JIT) compiler that enables graph compilation in PyTorch programs without sacrificing the flexibility of Python. It achieves this by dynamically modifying Python bytecode before execution and extracting sequences of PyTorch operations into an FX graph, which is then JIT compiled using one of many extensible backends. TorchInductor is the default compiler backend for TorchDynamo, which translates PyTorch programs into OpenAI's Triton for GPUs and C++ for CPUs. Results show that TorchDynamo is able to capture graphs more robustly than prior approaches while adding minimal overhead, and TorchInductor is able to provide a 2.27\u00d7 inference and 1.41\u00d7 training geometric mean speedup on an NVIDIA A100 GPU across 180+ real-world models, which outperforms six other compilers. These extensions provide a new way to apply optimizations through compilers in eager mode frameworks like PyTorch."}54{"work_id": 7129076810, "title": "Dual Memristor-Coupled Unidirectional Ring Neural Network: Abundant Hidden Firings and Application in Hardware Image Encryption", "abstract": "Due to the fixed weights and simple topology of traditional unidirectional ring neural networks, it is challenging to generate complex firing dynamics. This paper introduces two threshold memristor models to mimic neuronal autapses, constructing a novel dual memristor-coupled unidirectional ring neural network (DMCURNN). Benefiting from the dual memristor-based autapse emulation, the DMCURNN enriches diverse hidden firing dynamics that are absent in conventional unidirectional ring neural networks, and the richness and complexity of these hidden firing dynamics are highly sensitive to the memristive coupling strengths. Numerical simulations indicate that the DMCURNN is able to generate various hidden firing activities, pattern transitions, homogeneous firing multistability of infinite coexisting homogeneous firing patterns and heterogeneous firing multistability of seven heterogeneous firing patterns. Furthermore, through adjusting the coupling parameters along with the external current intensity, the regulatory mechanisms of hidden firing patterns and their amplitude and frequency regulation are revealed. Subsequently, the hidden firing dynamics of this network are verified on an FPGA hardware platform. Finally, Using the hidden firing multistability characteristic of the DMCURNN, a novel bitstream-based hardware image encryption scheme with a dynamic key update mechanism is designed. Performance evaluations demonstrate that the proposed scheme exhibits exceptional robustness and high efficiency for IoT security applications."}55{"work_id": 2952332632, "title": "Towards a Reliable and Context-Based System Architecture for Autonomous Vehicles", "abstract": "Full vehicle autonomy excludes a takeover by passengers in case a safety-critical application fails. Therefore, the system responsible for operating the autonomous vehicle has to detect and handle failures autonomously. Moreover, this system has to ensure the safety of the passengers, as well as the safety of other road users at any given time. Especially in the initial phase of autonomous vehicles, building up consumer confidence is essential. Therefore, in this regard, handling all failures by simply performing an emergency stop is not desirable. In this paper, we introduce an approach enabling a dynamic and safe reconfiguration of the autonomous driving system to handle occurring hardware and software failures. Since the requirements concerning safe reconfiguration actions are significantly affected by the current context the car is experiencing, the developed reconfiguration approach is sensitive to context changes. Our approach defines three interconnected layers, which are distinguished by their level of awareness. The top layer, referred to as the context layer, is responsible for observing the context. These context observations, in turn, imply a set of requirements, which constitute the input for the reconfiguration layer. The latter layer is required to determine reconfiguration actions, which are then executed by the architecture layer."}56{"work_id": 4405183524, "title": "Quantum error correction below the surface code threshold", "abstract": "Quantum error correction [1, 2, 3, 4] provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. Here, we present two below-threshold surface code memories on our newest generation of superconducting processors, Willow: a distance-7 code, and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $$\\varLambda \\mathrm{=2.14}\\pm 0.02$$ when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% \u00b1 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit\u2019s lifetime by a factor of $$2.4\\pm 0.3$$ . Our system maintains below-threshold performance when decoding in real time, achieving an average decoder latency of 63 \u03bcs at distance-5 up to a million cycles, with a cycle time of 1.1 \u03bcs. We also run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or $$3$$ $$\\times $$ $${10}^{9}$$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms."}57{"work_id": 3182301081, "title": "Zico: Efficient {GPU} Memory Sharing for Concurrent {DNN} Training", "abstract": "GPUs are the workhorse in modern server infrastructure fueling advances in a number of compute-intensive workloads such as deep neural network (DNN) training. Several recent works propose solutions on sharing GPU resources across multiple concurrent DNN training jobs, but none of them address rapidly increasing memory footprint introduced by such job co-locations, which greatly limit the effectiveness of sharing GPU resources. In this paper, we present Zico, the first DNN system that aims at reducing the system-wide memory consumption for concurrent training. Zico keeps track of the memory usage pattern of individual training job by monitoring its progress on GPU computations and makes memory reclaimed from the job globally sharable. Based on this memory management scheme, Zico automatically decides a strategy to share memory among concurrent jobs with minimum delay on training while not exceeding a given memory budget such as GPU memory capacity. Our evaluation shows that Zico outperforms existing GPU sharing approaches and delivers benefits over a variety of job co-location scenarios."}58{"work_id": 7128511721, "title": "A Memristor-Based Neural Network Circuit With Classical Conditioning and Fear Generalization", "abstract": "The new generation of artificial intelligence (AI) is evolving from perception-oriented processing toward cognition-oriented intelligence, emphasizing learning, memory, decision-making, and adaptive behavior inspired by the human brain. In this paper, a memristor-based perceptual decision-making neural network circuit (PDNNC) is proposed to integrate multiple classical conditioning mechanisms and fear generalization within a unified hardware framework. Unlike existing studies that focus on isolated learning behaviors, the proposed circuit architecture simultaneously realizes associative learning, forgetting, latent inhibition, blocking effects, secondary conditioning, and emotion-modulated decision-making at the circuit level. A voltage-controlled threshold-type memristor model is employed to emulate synaptic plasticity, while modular perceptual and decision-making subcircuits are designed to ensure scalability and functional consistency. The complete circuit implementation is verified using PSPICE circuit simulations. Simulation results demonstrate the correct realization of diverse classical conditioning behaviors, controllable fear generalization, and adaptive decision responses under different perceptual stimuli. The proposed bio-inspired circuit has vast potential applications in realizing cognitive intelligence and robotics."}59{"work_id": 4297813615, "title": "DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!", "abstract": "Deep Neural Networks (DNNs) are becoming common in \"learning-enabled\" time-critical applications such as autonomous driving and robotics. One approach to protect DNN inference from adversarial actions and preserve model privacy/confidentiality is to execute them within trusted enclaves available in modern processors. However, running DNN inference inside limited-capacity enclaves while ensuring timing guarantees is challenging due to (a) large size of DNN workloads and (b) extra switching between \"normal\" and \"trusted\" execution modes. This paper introduces new time-aware scheduling schemes - DeepTrust^RT - to securely execute deep neural inferences for learning-enabled real-time systems. We first propose a variant of EDF (called DeepTrust^RT-LW) that slices each DNN layer and runs them sequentially in the enclave. However, due to extra context switch overheads of individual layer slices, we further introduce a novel layer fusion technique (named DeepTrust^RT-FUSION). Our proposed scheme provides hard real-time guarantees by fusing multiple layers of DNN workload from multiple tasks; thus allowing them to fit and run concurrently within the enclaves while maintaining real-time guarantees. We implemented and tested DeepTrust^RT ideas on the Raspberry Pi platform running OP-TEE+DarkNet-TZ DNN APIs and three DNN workloads (AlexNet-squeezed, Tiny Darknet, YOLOv3-tiny). Compared to the layer-wise partitioning approach (DeepTrust^RT-LW), DeepTrust^RT-FUSION can schedule up to 3x more tasksets and reduce context switches by up to 11.12x. We further demonstrate the efficacy of DeepTrust^RT using a flight controller (ArduPilot) case study and find that DeepTrust^RT-FUSION retains real-time guarantees where DeepTrust^RT-LW becomes unschedulable."}60{"work_id": 4396986598, "title": "Quantum computing with Qiskit", "abstract": "We describe Qiskit, a software development kit for quantum information science. We discuss the key design decisions that have shaped its development, and examine the software architecture and its core components. We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits. Lastly, we discuss some of the ecosystem of tools and plugins that extend Qiskit for various tasks, and the future ahead."}61{"work_id": 4409568286, "title": "A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms", "abstract": "Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent developments in DL accelerators, focusing on their role in meeting the performance demands of HPC applications. We explore cutting-edge approaches to DL acceleration, covering not only GPU- and TPU-based platforms but also specialized hardware such as FPGA- and ASIC-based accelerators, Neural Processing Units, open hardware RISC-V-based accelerators, and co-processors. This survey also describes accelerators leveraging emerging memory technologies and computing paradigms, including 3D-stacked Processor-In-Memory, non-volatile memories like Resistive RAM and Phase Change Memories used for in-memory computing, as well as Neuromorphic Processing Units, and Multi-Chip Module-based accelerators. Furthermore, we provide insights into emerging quantum-based accelerators and photonics. Finally, this survey categorizes the most influential architectures and technologies from recent years, offering readers a comprehensive perspective on the rapidly evolving field of deep learning acceleration."}62{"work_id": 2156094048, "title": "Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach", "abstract": "This paper presents generalizations of Bayes likelihood-ratio updating rule which facilitate an asynchronous propagation of the impacts of new beliefs and/or new evidence in hierarchically organized inference structures with multi-hypotheses variables. The computational scheme proposed specifies a set of belief parameters, communication messages and updating rules which guarantee that the diffusion of updated beliefs is accomplished in a single pass and complies with the tenets of Bayes calculus."}63{"work_id": 1521266393, "title": "Circuit quantum electrodynamics", "abstract": "This review surveys the development over the last 15 years of circuit quantum electrodynamics, the nonlinear quantum optics of microwave electrical circuits. In analogy to cavity quantum electrodynamics, lasers are replaced by rf signal generators, optical cavities by superconducting resonators, and atoms by superconducting qubits. Circuit QED offers enhanced light-matter coupling in which strong quantum optical nonlinearities are observable at the level of individual photons. This new parameter regime leads to unique capabilities for fundamental studies in quantum optics, nearly ideal quantum-limited measurements, and quantum computation."}64{"work_id": 7138433923, "title": "TokenPowerBench: Benchmarking the Power Consumption of LLM Inference", "abstract": "Large language model (LLM) services now answer billions of queries per day, and industry reports show that inference, not training, accounts for more than 90% of total power consumption. However, existing benchmarks focus on either training/fine-tuning or performance of inference and provide little support for power consumption measurement and analysis of inference. We introduce TokenPowerBench, the first lightweight and extensible benchmark designed for LLM-inference power consumption studies. The benchmark combines a declarative configuration interface covering model choice, prompt set, and inference engine, a measurement layer that captures GPU-, node-, and system-level power without specialized power meters, and a phase-aligned metrics pipeline that attributes energy to the prefill and decode stages of every request. These elements make it straightforward to explore the power consumed by an LLM inference run; furthermore, by varying batch size, context length, parallelism strategy and quantization, users can quickly assess how each setting affects joules per token and other energy-efficiency metrics. We evaluate TokenPowerBench on four of the most widely used model series (Llama, Falcon, Qwen, and Mistral). Our experiments cover from 1 billion parameters up to the frontier-scale Llama3-405B model. Furthermore, we release TokenPowerBench as open source to help users to measure power consumption, forecast operating expenses, and meet sustainability targets when deploying LLM services."}65{"work_id": 2975712713, "title": "A generic communication scheduler for distributed DNN training acceleration", "abstract": "We present ByteScheduler, a generic communication scheduler for distributed DNN training acceleration. ByteScheduler is based on our principled analysis that partitioning and rearranging the tensor transmissions can result in optimal results in theory and good performance in real-world even with scheduling overhead. To make ByteScheduler work generally for various DNN training frameworks, we introduce a unified abstraction and a Dependency Proxy mechanism to enable communication scheduling without breaking the original dependencies in framework engines. We further introduce a Bayesian Optimization approach to auto-tune tensor partition size and other parameters for different training models under various networking conditions. ByteScheduler now supports TensorFlow, PyTorch, and MXNet without modifying their source code, and works well with both Parameter Server (PS) and all-reduce architectures for gradient synchronization, using either TCP or RDMA. Our experiments show that ByteScheduler accelerates training with all experimented system configurations and DNN models, by up to 196% (or 2.96X of original speed)."}66{"work_id": 7128293306, "title": "Neuromorphic photonic computing with an electro-optic analog memory", "abstract": "In neuromorphic photonic systems, device operations are typically governed by analog signals, necessitating digital-to-analog converters (DAC) and analog-to-digital converters (ADC). However, data movement between memory and these converters in conventional von Neumann architectures incur significant energy costs. We propose an analog electronic memory co-located with photonic computing units to eliminate repeated long-distance data movement. Here, we demonstrate a monolithically integrated neuromorphic photonic circuit with on-chip capacitive analog memory and evaluate its performance in machine learning for in situ training and inference using the MNIST dataset. Our analysis shows that integrating analog memory into a neuromorphic photonic architecture can achieve over 26 \u00d7 power savings compared to conventional SRAM-DAC architectures. Furthermore, maintaining a minimum analog memory retention-to-network-latency ratio of 100 maintains >90% inference accuracy, enabling leaky analog memories without substantial performance degradation. This approach reduces reliance on DACs, minimizes data movement, and offers a scalable pathway toward energy-efficient, high-speed neuromorphic photonic computing."}67{"work_id": 2930508541, "title": "Cloud Programming Simplified: A Berkeley View on Serverless Computing", "abstract": "Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud. It provides an interface that greatly simplifies cloud programming, and represents an evolution that parallels the transition from assembly language to high-level programming languages. This paper gives a quick history of cloud computing, including an accounting of the predictions of the 2009 Berkeley View of Cloud Computing paper, explains the motivation for serverless computing, describes applications that stretch the current limits of serverless, and then lists obstacles and research opportunities required for serverless computing to fulfill its full potential. Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate the future of cloud computing."}68{"work_id": 3016932526, "title": "Q uantum ESPRESSO toward the exascale", "abstract": "Quantum ESPRESSO is an open-source distribution of computer codes for quantum-mechanical materials modeling, based on density-functional theory, pseudopotentials, and plane waves, and renowned for its performance on a wide range of hardware architectures, from laptops to massively parallel computers, as well as for the breadth of its applications. In this paper, we present a motivation and brief review of the ongoing effort to port Quantum ESPRESSO onto heterogeneous architectures based on hardware accelerators, which will overcome the energy constraints that are currently hindering the way toward exascale computing."}69{"work_id": 4402694963, "title": "Optical neural networks: progress and challenges", "abstract": "Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware. However, conventional computing hardware is inefficient at implementing complex tasks, in large part because the memory and processor in its computing architecture are separated, performing insufficiently in computing speed and energy consumption. In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as sub-nanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements. Then, we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip components. Finally, we summarize and discuss the computational density, nonlinearity, scalability, and practical applications of ONNs, and comment on the challenges and perspectives of the ONNs in the future development trends."}70{"work_id": 4321649614, "title": "Quantum-selected configuration interaction: Classical diagonalization of Hamiltonians in subspaces selected by quantum computers", "abstract": "We propose quantum-selected configuration interaction, a class of hybrid quantum-classical algorithms for calculating the ground- and excited-state energies of many-electron Hamiltonians on noisy quantum devices. Suppose that an approximate ground state can be prepared on a quantum computer either by variational quantum eigensolver or by some other method. Then, by sampling the state in the computational basis, which is hard for classical computation in general, one can identify the electron configurations that are important for reproducing the ground state. The Hamiltonian in the subspace spanned by those important configurations is diagonalized on classical computers to output the ground-state energy and the corresponding eigenvector. The excited-state energies can be obtained similarly. The result is robust against statistical and physical errors because the noisy quantum devices are used only to define the subspace, and the resulting ground-state energy strictly satisfies the variational principle even in the presence of such errors. The expectation values of various other operators can also be estimated for obtained eigenstates with no additional quantum cost, since the explicit eigenvectors in the subspaces are known. We verified our proposal by numerical simulations and demonstrated it on a quantum device for an eight-qubit molecular Hamiltonian. The proposed algorithms are potentially feasible to tackle some challenging molecules by exploiting quantum devices with several tens of qubits, assisted by high-performance classical computing resources for diagonalization."}71{"work_id": 4386978266, "title": "Accelerated Systems for Portable DNA Sequencing", "abstract": "The MinION is a revolutionary handheld DNA sequencer that is inexpensive, portable, and can perform real-time sequencing. MinION is increasingly used in Precision Medicine applications. However, the MinION lacks portable compute power. This thesis introduces two clinical applications of the MinION and identifies and solves performance bottlenecks through hardware and software solutions to enable portable microbial diagnostics. Finally, we discuss how our accelerated solutions will fit on a laptop with a GPU. More than 99% of DNA fragments in a typical human sample are non-target (human), which may be skipped in real-time using the MinION\u2019s Read Until feature. We analyze the performance of the Read Until pipeline in detecting target microbial species for targeted viral pathogen detection and microbiome abundance estimation. We find new sources of performance bottlenecks (basecaller in the classification of a fragment) that are not addressed by past genomics accelerators. While SquiggleFilter and DTWax are our solutions for targeted viral pathogen detection, RawMap is for untargeted microbiome analysis. We also discuss accelerating the bottleneck step in the DNA mapping software (Minimap2) used in all of MinION\u2019s sequencing workflows. For targeted virus detection, we discuss SquiggleFilter which is a portable and programmable hardware-software solution that directly analyzes MinION\u2019s raw squiggles and filters everything except target viral DNA fragments. SquiggleFilter avoids the expensive basecalling step and uses hardware-accelerated subsequence Dynamic Time Warping (sDTW). We show that our 14.3W 13.25mm2 accelerator has 274\u00d7 greater throughput and 3481\u00d7 lower latency than existing GPU-based solutions while consuming half the power. DTWax overcomes the on-chip memory limitations of SquiggleFilter by optimizing its high-accuracy sDTW algorithm for GPUs resulting in a \u223c1.92X sequencing speedup and \u223c3.64X compute speedup: costup. For the untargeted classification and analysis of microbiome, we discuss RawMap which is a machine-learning-based smart and efficient solution that reduces sequencing time and cost by \u223c24% and computing cost by \u223c22%. We also discuss how RawMap may be used as an alternative to the RT-PCR test for viral load quantification of SARS-CoV-2. Minimap2 is a software used in all MinION workflows. minimap2-accelerated (mm2-ax) is a heterogeneous design for sequence mapping where minimap2\u2019s bottleneck step is sped up on the GPU with bit-exact output. mm2-ax on an NVIDIA A100 GPU improves the bottleneck step (chaining) with 4.07 - 1.93X speedup: costup over a SIMD baseline. Finally, we envision a portable solution to microbial diagnostics with a laptop connected to the MinION. DTWax can perform targeted virus detection on the GPU and RawMap does untargeted microbiome classification on the CPU. Post-sequencing tasks like basecalling, alignment (using mm2-ax) and variant calling use the GPU."}72{"work_id": 2273211146, "title": "The multiprocessor real-time scheduling of general task systems", "abstract": "The recent emergence of multicore and related technologies in many commercial systems has increased the prevalence of multiprocessor architectures. Contemporaneously, real-time applications have become more complex and sophisticated in their behavior and interaction. Inevitably, these complex real-time applications will be deployed upon these multiprocessor platforms and require temporal analysis techniques to verify their correctness. However, most prior research in multiprocessor real-time scheduling has addressed the temporal analysis only of Liu and Layland task systems. The goal of this dissertation is to extend real-time scheduling theory for multiprocessor systems by developing temporal analysis techniques for more general task models such as the sporadic task model, the generalized multiframe task model, and the recurring real-time task model. The thesis of this dissertation is: Optimal online multiprocessor real-time scheduling algorithms for sporadic and more general task systems are impossible; however, efficient, online scheduling algorithms and associated feasibility and schedulability tests, with provably bounded deviation from any optimal test, exist. To support our thesis, this dissertation develops feasibility and schedulability tests for various multiprocessor scheduling paradigms. We consider three classes of multiprocessor scheduling based on whether a real-time job may migrate between processors: full-migration, restricted-migration, and partitioned. For all general task systems, we obtain feasibility tests for arbitrary real-time instances under the full-and restricted-migration paradigms. Despite the existence of tests for feasibility, we show that optimal online scheduling of sporadic and more general systems is impossible. Therefore, we focus on scheduling algorithms that have constant-factor approximation ratios in terms of an analysis technique known as resource augmentation. We develop schedulability tests for scheduling algorithms, earliest-deadline-first (edf) and deadline-monotonic (dm), under full-migration and partitioned scheduling paradigms. Feasibility and schedulability tests presented in this dissertation use the workload metrics of demand-based load and maximum job density and have provably bounded deviation from optimal in terms of resource augmentation. We show the demand-based load and maximum job density metrics may be exactly computed in pseudo-polynomial time for general task systems and approximated in polynomial time for sporadic task systems."}73{"work_id": 3174122836, "title": "Quantum Computing 40 Years Later", "abstract": "This chapter sketches the foundations of the subject, discussing salient features of quantum information, formulates a mathematical model of quantum computation and highlights some implications of the model. It reviews two particularly promising applications of quantum computing foreseen by Feynman, simulating the dynamics of complex quantum systems, and computing their static properties. The chapter explains the concept of quantum error correction, the basis of belief that quantum computers can be scaled up to large systems that solve very hard problems. Shor&s;s discovery, and its obvious implications for cryptanalysis, caused interest in quantum computing to skyrocket. Because of Shor&s;s algorithm, the public key cryptographic protocols the people use to protect privacy when they communicate over the Internet will become vulnerable to attacks by quantum computers in the future. Classical computers are especially bad at simulating quantum dynamics \u2013 that is, predicting how a highly entangled quantum state will change with time."}74{"work_id": 4407592994, "title": "Green Federated Learning: A New Era of Green Aware AI", "abstract": "The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly, machine learning is acknowledged as one of today\u2019s most energy-intensive computational applications, posing a significant challenge to the environmental sustainability of next-generation intelligent systems. Achieving environmental sustainability entails ensuring that every AI algorithm is designed with sustainability in mind, integrating green considerations from the architectural phase onwards. Recently, Federated Learning (FL), with its distributed nature, presents new opportunities to address this need. Hence, it is imperative to elucidate the potential and challenges stemming from recent FL advancements and their implications for sustainability. Moreover, it is crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works and assessing their contributions to green-aware artificial intelligence for sustainable environments, with a specific focus on IoT research. It delves into current issues in green federated learning from an energy-efficient standpoint, discussing potential challenges and future prospects for green IoT application research."}75{"work_id": 4406650295, "title": "AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration", "abstract": "Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce cloud computing costs and protect users' privacy. However, the astronomical model size and the limited hardware resources pose significant deployment challenges. To solve these issues, we propose Activation-aware Weight Quantization (AWQ) and TinyChat, an algorithm-system full-stack solution for efficient on-device LLM deployment. AWQ is a novel quantization method that identifies and protects salient weights based on activation distribution, significantly reducing model size while preserving performance. TinyChat, an optimized inference framework, translates AWQ's theoretical memory savings into practical speedups through techniques such as on-the-fly dequantization, SIMD-aware weight packing, and kernel fusion. Together, they enable 4x model size reduction and 3-4x acceleration across various edge platforms, from high-end desktop GPUs to resource-constrained IoT devices. This solution democratizes on-device LLM deployment, offering privacy-preserving, low-latency AI capabilities across a wide range of applications."}76{"work_id": 4213212652, "title": "Noisy intermediate-scale quantum algorithms", "abstract": "Noisy quantum computers can in principle perform reliable quantum computations, but truly scalable systems require noise levels lower than are presently achieved. Still, moderate-complexity computations can be performed. This review discusses what is possible in this ``noisy intermediate scale'' quantum (NISQ) era. Topic areas include the simulation of many-body physics and chemistry, combinatorial optimization, and machine learning. It is evident that the NISQ era has produced new paradigms for programming that will be built upon as quantum computers are further perfected."}77{"work_id": 7130516837, "title": "Gaussian-Function-Driven Chaotic Memristive Dynamics with Application to Image Encryption", "abstract": "This paper develops a Gaussian-function-driven chaotic memristive system in which the magnetic flux is characterized by a Gaussian function, distinguishing it from prevailing formulations that employ piecewise, polynomial, or weakly smooth nonlinearities. The proposed system exhibits the key fingerprints of memristive behavior, including pinched hysteresis loops, local activity and nonvolatility. Integrating the Gaussian function into Chua\u2019s circuit produces a multiscroll chaotic Gaussian memristive system, where the attractors emerge in an organized and predictable pattern, with their numbers and spatial distribution determined by the Gaussian kernel parameters. To demonstrate the applicability of the proposed chaotic system, an image encryption scheme exploits the chaotic sequences generated by this system and integrates adaptive Zigzag scrambling, DNA encoding and bit-plane cross-diffusion. Experimental results show that the scheme achieves uniform histograms, negligible pixel correlation, strong key sensitivity and high resistance to noise and differential attacks. The incorporation of Gaussian function enriches the framework of chaotic memristive systems and establishes a flexible and robust foundation for secure image encryption."}78{"work_id": 3046751266, "title": "Scalable molecular dynamics on CPU and GPU architectures with NAMD", "abstract": "NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu."}79{"work_id": 4403458785, "title": "Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs", "abstract": "Abstract Faculty perspectives on the use of artificial intelligence (AI) in higher education are crucial for AI\u2019s meaningful integration into teaching and learning, yet research is scarce. This paper presents a study designed to gain insight into faculty members\u2019 ( N = 122) AI self-efficacy and distinct latent profiles, perceived benefits, challenges, use, and professional development needs related to AI. The respondents saw greater equity in education as AI\u2019s greatest benefit, while students and faculty members\u2019 lack of AI literacy was among the greatest challenges, with the majority interested in professional development. Latent class analysis revealed four distinct faculty member profiles: optimistic, critical, critically reflected, and neutral. The optimistic profile moderates the relationship between self-efficacy and usage. The development of adequate support services is suggested for successful and sustainable digital transformation."}80{"work_id": 4308245305, "title": "Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model", "abstract": "Progress in machine learning (ML) comes with a cost to the environment, given that training ML models requires significant computational resources, energy and materials. In the present article, we aim to quantify the carbon footprint of BLOOM, a 176-billion parameter language model, across its life cycle. We estimate that BLOOM's final training emitted approximately 24.7 tonnes of~\\carboneq~if we consider only the dynamic power consumption, and 50.5 tonnes if we account for all processes ranging from equipment manufacturing to energy-based operational consumption. We also study the energy requirements and carbon emissions of its deployment for inference via an API endpoint receiving user queries in real-time. We conclude with a discussion regarding the difficulty of precisely estimating the carbon footprint of ML models and future research directions that can contribute towards improving carbon emissions reporting."}81{"work_id": 1224547023, "title": "Model-Based Framework for Schedulability Analysis Using Uppaal 4.1", "abstract": "Embedded systems involve the monitoring and control of complex physical processes using applications running on dedicated execution platforms in a resource-constrained manner in terms of, for example, memory, processing power, bandwidth, energy consumption, and timing behavior."}82{"work_id": 4286840061, "title": "Algorithm-Architecture Co-Design for Domain-Specific Accelerators in Communication and Artificial Intelligence", "abstract": "The past decade has witnessed an explosive growth of data and the needs for high-speed data communications and processing. The needs continue to drive the development of new hardware for transmitting more data reliably and processing more data to obtain a higher level of intelligence. This thesis work explores algorithm-architecture co-design approach to derive efficient solutions for domain-specific communication and machine learning accelerators. It focuses on advanced and most compute-intensive accelerator designs for: 1) channel coding for data transmission, including polar codes and low-density parity-check (LDPC) codes, and 2) neural networks for machine learning, including differentiable neural computer (DNC) and neural ordinary differential equations (NODE). It also covers an interdisciplinary area of AI-aided communication, exploring DNC-aided flip decoding for polar codes. This work introduces a split-tree successive-cancellation list (SCL) decoder that works by dividing a polar code's decoding tree to sub-trees following a split-tree algorithm. Through algorithm-architecture co-optimizations, a 0.64mm2 40nm test chip implements a split-4, list-2, 8-frame-interleaved decoder that supports configurable code lengths up to 1024 bit and variable code rates. This work advances LDPC codes in two aspects: 1) we take the inspiration from simulated annealing to generalize the post-processor design using three methods: quenching, extended heating, and focused heating, each of which targets a different decoding error structure. The resulting post-processor is demonstrated to lower the error rate by two orders of magnitudes. 2) We also present a fully parallel decoder for a (160, 80) regular-(2, 4) NB-LDPC code over Galois field GF(64) in 65nm CMOS. The decoder employs fine-grained dynamic clock gating and decoder early termination to achieve a throughput of 1.22Gb/s and an energy efficiency of 3.03nJ/b. This work contributes to the hardware acceleration of DNC. We present HiMA, a tiled, history-based memory access engine with distributed memories in tiles. HiMA incorporates a traffic-aware multi-mode network-on-chip (NoC), an optimal submatrix-based memory partition and a two-stage usage sort method leveraging distributed tiles. We create a distributed DNC (DNC-D) to allow almost all memory operations to be applied to local memories. In a 40nm design, HiMA running DNC and DNC-D demonstrates 39.1x higher speed, 164.3x better area efficiency, and 61.2x better power efficiency over the state-of-the-art accelerators. This work contributes to the hardware acceleration of NODE for improved modeling capability of continuous-time events. We carry out algorithm-architecture co-design: 1) we propose adaptive neural activation sparsity for up to 80% complexity reduction while maintaining excellent training accuracy. 2) we develop a multi-mode PE design for NODE compute kernels with configurable interconnects between PEs to handle a variety of numerical ODE solvers. The hardware efficiency is further enhanced by exploring hardware reuse and hierarchical memory. Lastly, this work investigates an interdisciplinary area of AI-aided communication, applying DNC for flip decoding of polar codes. We develop new state and action encoding with a two-phase decoding flow. Simulation results show that proposed DNC-aided SCL-Flip (DNC-SCLF) decoding demonstrates up to 0.34dB coding gain improvement or 54.2% reduction in average number of decoding attempts compared to prior works. The five pieces of work presented in this thesis tackle hardware acceleration challenges in domain-specific computing using algorithm-architecture co-design techniques. The results of this work contribute to the developments of hardware accelerators in channel coding and deep learning, enabling next-generation communication and artificial intelligence from theory to efficient hardware."}83{"work_id": 7130433466, "title": "A Second-Order Memristor Method to Construct Memristive Neural Networks With Multi-Butterfly and Multi-Scroll Dynamics", "abstract": "Memristor-based Hopfield neural networks (MHNNs) exhibit rich chaotic dynamics and bear closer hardware resemblance to the biological brain, making them well suited for emulating neural dynamical behaviors. However, most existing MHNNs are constructed with first-order memristors. This paper proposes a novel second-order memristor (SOM) approach for constructing MHNNs with enriched chaotic dynamics. Specifically, a second-order memristor is incorporated into a three-neuron Hopfield neural network to emulate the magnetic coupling mechanism between neurons, thereby forming a second-order memristor-based neural network (SOM-HNN). Comprehensive dynamical analyses, including bifurcation diagrams, Lyapunov exponent spectra, and numerical simulations, confirm that the proposed SOM-HNN exhibits richer and more intricate chaos behaviors than its first-order counterparts. Remarkably, the proposed SOM-HNN can simultaneously generate butterfly and scroll attractors, multi-butterfly and multi-scroll attractors, as well as initial-boosed coexisting multi-butterfly and multi-scroll attractors, thereby substantially enhancing its dynamical diversity. To the best of our knowledge, this is the first report of both multi-butterfly and multi-scroll dynamics in a neural network. Furthermore, the SOM-HNN is implemented in hardware using analog circuits and a digital field-programmable gate array (FPGA) platform. Experimental results demonstrate the network\u2019s abundant dynamical features and its feasibility for efficient hardware realization in neuromorphic engineering applications."}84{"work_id": 7135102590, "title": "Biologically Plausible Memristive Decision-Making Circuit for Adaptive Control in Industrial Autonomous Navigation", "abstract": "In biological decision-making, adaptive behavior arises from the interaction between structured task context, expectation, action selection, and feedback-based learning. While most existing studies reproduce reward-driven responses under clear sensory stimuli, decision formation under weak or absent sensory evidence, such as low or 0% contrast conditions, remains insufficiently explored. To address this issue, this work proposes a biologically plausible memristive decision-making framework based on a block-structured task paradigm. The proposed system adopts a closed-loop architecture composed of four functional modules: stimulus, expectation, action, and reward/punishment. Sensory information is encoded when available, while the expectation pathway provides prior-guided modulation when sensory evidence becomes weak or unreliable. Action selection is generated through competitive integration, and reward\u2013punishment feedback dynamically corrects decision bias and reinforces appropriate responses. Through this hierarchical interaction, stable decision behavior can be achieved even in the absence of explicit sensory inputs. PSPICE simulations are conducted to analyze system dynamics and validate the corrective role of the reward\u2013punishment mechanism under weak and ambiguous conditions. In addition, the proposed framework is demonstrated in an industrial autonomous navigation scenario, illustrating its scalability and applicability for adaptive decision-making under uncertainty."}85{"work_id": 4394727812, "title": "Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence", "abstract": "The pursuit of artificial general intelligence (AGI) continuously demands higher computing performance. Despite the superior processing speed and efficiency of integrated photonic circuits, their capacity and scalability are restricted by unavoidable errors, such that only simple tasks and shallow models are realized. To support modern AGIs, we designed Taichi-large-scale photonic chiplets based on an integrated diffractive-interference hybrid design and a general distributed computing architecture that has millions-of-neurons capability with 160-tera-operations per second per watt (TOPS/W) energy efficiency. Taichi experimentally achieved on-chip 1000-category-level classification (testing at 91.89% accuracy in the 1623-category Omniglot dataset) and high-fidelity artificial intelligence-generated content with up to two orders of magnitude of improvement in efficiency. Taichi paves the way for large-scale photonic computing and advanced tasks, further exploiting the flexibility and potential of photonics for modern AGI."}86{"work_id": 4385481791, "title": "Reducing the Carbon Impact of Generative AI Inference (today and in 2035)", "abstract": "Generative AI, exemplified in ChatGPT, Dall-E 2, and Stable Diffusion, are exciting new applications consuming growing quantities of computing. We study the compute, energy, and carbon impacts of generative AI inference. Using ChatGPT as an exemplar, we create a workload model and compare request direction approaches (Local, Balance, CarbonMin), assessing their power use and carbon impacts."}87{"work_id": 7135078329, "title": "Highly Reliable RRAM-Based Physical Unclonable Function With Auto-Write Technique", "abstract": "Physical unclonable functions (PUFs) have attracted significant attention for high-security applications, particularly in the Internet of Things (IoT). While resistive random access memory (RRAM)-based PUF technology offers excellent energy efficiency and integration density, its reliability remains a major challenge. This work proposes a highly reliable RRAM-based PUF that utilizes the 4T2R cross-coupled PUF cell to extract the response. Additionally, an automatic write-back technique is introduced to enhance the initial resistance mismatch in RRAM, thereby improving the PUF reliability. Experimental results demonstrate that the proposed PUF achieves outstanding performance, with near-ideal uniqueness (50.02%), uniformity (50.11%), and robust randomness (passing NIST 800-22 tests). The design exhibits robust reliability across a wide operating range (from$- 40~^{\\circ }$C to$120~^{\\circ }$C and 0.7 to 1.4 V), achieving a bit error rate (BER) of$\\lt 4.89\\times 10^{-7}$. Furthermore, the energy consumption per bit is optimized to 4.8 fJ at 1.1 V,$25~^{\\circ }$C, showing its suitability for energy-constrained applications."}88{"work_id": 2963840171, "title": "Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives", "abstract": "In September 2015, the United Nations General Assembly accepted the 2030 Development Agenda, which has included 92 paragraphs, and the Paragraph 91 defined 17 sustainable development goals (SDGs) and 169 associated targets. The goal of this paper is to discover the correlations among SDGs and information and communications technologies (ICTs). This paper discusses the roles and opportunities that ICTs play in pursuing the SDGs. We identify a number of research gaps to those three pillars, social, economic, and environmental perspectives, of sustainable development. After extensive literature reviews on the SDG-related research initiatives and activities, we find that the majority of contributions to SDGs recognized by the IEEE and ACM research communities have mainly focused on the technical aspects while there are lack of the holistic social good perspectives. Therefore, there are essential and urgent needs to raise the awareness and call for attentions on how to innovate and energize ICTs in order to best assist all nations to achieve the SDGs by 2030."}89{"work_id": 4389373471, "title": "Logical quantum processor based on reconfigurable atom arrays", "abstract": "Abstract Suppressing errors is the central challenge for useful quantum computing 1 , requiring quantum error correction (QEC) 2\u20136 for large-scale processing. However, the overhead in the realization of error-corrected \u2018logical\u2019 qubits, in which information is encoded across many physical qubits for redundancy 2\u20134 , poses substantial challenges to large-scale logical quantum computing. Here we report the realization of a programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits. Using logical-level control and a zoned architecture in reconfigurable neutral-atom arrays 7 , our system combines high two-qubit gate fidelities 8 , arbitrary connectivity 7,9 , as well as fully programmable single-qubit rotations and mid-circuit readout 10\u201315 . Operating this logical processor with various types of encoding, we demonstrate improvement of a two-qubit logic gate by scaling surface-code 6 distance from d = 3 to d = 7, preparation of colour-code qubits with break-even fidelities 5 , fault-tolerant creation of logical Greenberger\u2013Horne\u2013Zeilinger (GHZ) states and feedforward entanglement teleportation, as well as operation of 40 colour-code qubits. Finally, using 3D [[8,3,2]] code blocks 16,17 , we realize computationally complex sampling circuits 18 with up to 48 logical qubits entangled with hypercube connectivity 19 with 228 logical two-qubit gates and 48 logical CCZ gates 20 . We find that this logical encoding substantially improves algorithmic performance with error detection, outperforming physical-qubit fidelities at both cross-entropy benchmarking and quantum simulations of fast scrambling 21,22 . These results herald the advent of early error-corrected quantum computation and chart a path towards large-scale logical processors."}90{"work_id": 2788945937, "title": "Classification with Quantum Neural Networks on Near Term Processors", "abstract": "We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network\u2019s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation."}91{"work_id": 4386285839, "title": "Managing the Strategic Transformation of Higher Education through Artificial Intelligence", "abstract": "Considering the rapid advancements in artificial intelligence (AI) and their potential implications for the higher education sector, this article seeks to critically evaluate the strategic adoption of AI in the framework of \u201csmart universities\u201d. We envisage these innovative institutions as the imminent evolution in higher education, harnessing AI and quantum technologies to reshape academic and administrative processes. The core presumption is that through such integration, universities can achieve personalized learning trajectories, enhanced accessibility, economic efficiency, and a boost in overall operational performance. However, venturing into this new educational paradigm necessitates a thorough exploration of potential pitfalls, including questions surrounding educational quality, potential job losses, risks of bias, privacy breaches, and safety concerns. Our primary objective is to offer a balanced assessment to aid stakeholders in making informed strategic decisions about endorsing and advancing the smart university model. A pivotal factor in this discourse is the acceptance of qualifications from AI-enriched institutions by employers, a variable that may drastically redefine the education sector\u2019s trajectory. Within the context of a comprehensive analysis of its broader societal impact, this article also delves into the ramifications of AI-driven innovations for historically Black colleges and universities (HBCUs)."}92{"work_id": 3099878876, "title": "Array programming with NumPy", "abstract": "Abstract Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis."}93{"work_id": 4366587887, "title": "Promising Emerging Technologies for Teaching and Learning: Recent Developments and Future Challenges", "abstract": "As time goes on and the number of people who use information and communication technology (ICT) grows, emerging technologies are receiving a lot of attention from academics, researchers, and users. Emerging technologies are changing more quickly than ever, and it is important to start building skills early in education by using the right examples to teach and learn about digital citizenship. New pedagogies support individual teaching and learning methodologies for knowledge acquisition and self-directed learning. Hundreds of digital education tools have been developed to increase student autonomy, enhance academic process management, promote collaboration, and improve communication between teachers and students. This article examines the current state of emerging technologies from a variety of educational viewpoints, highlights a few of them, and discusses both their established and potential educational benefits. Additionally, it offers in-depth debates on recent developments and future challenges from a wide range of perspectives. The analysis focuses on peer-reviewed research articles published in well-renowned publications databases over the last 8 years, drawing upon a bibliometric analysis using VOSviewer. The results of this study are also anticipated to serve as a substantial impetus for other universities and colleges worldwide to utilize innovatively and revolutionized technology for better academic performance."}94{"work_id": 3092732687, "title": "PENGEMBANGAN SISTEM INFORMASI PENERIMAANMAHASISWA BARU DENGAN METODE BUSINESSPROCESS AUTOMATION (STUDI KASUS: INSTITUTTEKNOLOGI KALIMANTAN)", "abstract": "Institut Teknologi Kalimantan (ITK) merupakan salah satu perguruan tinggi negeri baru di Indonesia. Salah satu proses bisnis yang ada di ITK yaitu proses\r\npenerimaan mahasiswa baru, ITK sudah melakukan proses penerimaan mahasiswa baru sejak tahun 2012 sebanyak 100 mahasiswa, dan pada tahun 2018 ITK menerima kurang lebih 1000 mahasiswa. Hal ini menunjukkan adanya peningkatan penerimaan mahasiswa baru yang cukup signifikan. Pada proses penerimaan mahasiswa baru di ITK belum ada sistem informasi yangdapat memanajemen data\r\nmahasiswa baru tersebut dan saat ini terdapat proses pengumpulan dan verifikasi\r\nberkas yang masih dilakukan menggunakan email dimana terdapat beberapa kendala dalam penggunaan email tersebut. Oleh karena itu, dalam penelitian ini\r\ndilakukan pengembangkan sistem informasi penerimaan mahasiswa baru berbasis web, yang dapat diakses oleh mahasiswa baru, verifikatur, dan admin selama proses\r\npendaftaran ulang mahasiswa baru. Metode yang digunakan dalam penelitian ini adalah Business Proses Automation menggunakan tools ProcessMaker. ProcessMaker merupakan sebuah aplikasi open source yang berguna untuk memanajemen alur kerja Business Process Management (BPM) dan solusi perangkat lunak berbasis web. Penelitian ini menghasilkan sistem informasi penerimaan mahasiswa baru berbasis web, yang dapat memanajemen datamahasiswa baru. Terdapat 14 proses pendaftaran ulang mahasiswa baru dan rekapdata yang dibedakan untuk masing-masing program studi di ITK, kemudian ada 1 proses untuk menjalankan fungsi impor data users menggunakan file .csv. Sistem\r\ninformasi ini sudah mulai digunakan ITK dalam proses penerimaan mahasiswa baru\r\njalur Seleksi Nasional Masuk Perguruan Tinggi Negeri (SNMPTN) Tahun 2019."}95{"work_id": 25727798, "title": "The Lustre Storage Architecture", "abstract": "This lengthy document often referred to as the \"Lustre Book\", contains a detailed outline of Lustre file system architecture, as it was created between 2001 and 2005, in accordance with the requirements from various users. Now, in 2019, most features have been implemented, but some only recently, and some along different lines of thought."}96{"work_id": 2972087877, "title": "Demystifying Parallel and Distributed Deep Learning", "abstract": "Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning."}97{"work_id": 4407158326, "title": "Distributed quantum computing across an optical network link", "abstract": "Abstract Distributed quantum computing (DQC) combines the computing power of multiple networked quantum processing modules, ideally enabling the execution of large quantum circuits without compromising performance or qubit connectivity 1,2 . Photonic networks are well suited as a versatile and reconfigurable interconnect layer for DQC; remote entanglement shared between matter qubits across the network enables all-to-all logical connectivity through quantum gate teleportation (QGT) 3,4 . For a scalable DQC architecture, the QGT implementation must be deterministic and repeatable; until now, no demonstration has satisfied these requirements. Here we experimentally demonstrate the distribution of quantum computations between two photonically interconnected trapped-ion modules. The modules, separated by about two metres, each contain dedicated network and circuit qubits. By using heralded remote entanglement between the network qubits, we deterministically teleport a controlled-Z (CZ) gate between two circuit qubits in separate modules, achieving 86% fidelity. We then execute Grover\u2019s search algorithm 5 \u2014to our knowledge, the first implementation of a distributed quantum algorithm comprising several non-local two-qubit gates\u2014and measure a 71% success rate. Furthermore, we implement distributed iSWAP and SWAP circuits, compiled with two and three instances of QGT, respectively, demonstrating the ability to distribute arbitrary two-qubit operations 6 . As photons can be interfaced with a variety of systems, the versatile DQC architecture demonstrated here provides a viable pathway towards large-scale quantum computing for a range of physical platforms."}98{"work_id": 3152809665, "title": "Nightcore: efficient and scalable serverless computing for latency-sensitive, interactive microservices", "abstract": "The microservice architecture is a popular software engineering approach for building flexible, large-scale online services. Serverless functions, or function as a service (FaaS), provide a simple programming model of stateless functions which are a natural substrate for implementing the stateless RPC handlers of microservices, as an alternative to containerized RPC servers. However, current serverless platforms have millisecond-scale runtime overheads, making them unable to meet the strict sub-millisecond latency targets required by existing interactive microservices."}99{"work_id": 4399913870, "title": "A review of green artificial intelligence: Towards a more sustainable future", "abstract": "Green artificial intelligence (AI) is more environmentally friendly and inclusive than conventional AI, as it not only produces accurate results without increasing the computational cost but also ensures that any researcher with a laptop can perform high-quality research without the need for costly cloud servers. This paper discusses green AI as a pivotal approach to enhancing the environmental sustainability of AI systems. Described are AI solutions for eco-friendly practices in other fields (green-by AI), strategies for designing energy-efficient machine learning (ML) algorithms and models (green-in AI), and tools for accurately measuring and optimizing energy consumption. Also examined are the role of regulations in promoting green AI and future directions for sustainable ML. Underscored is the importance of aligning AI practices with environmental considerations, fostering a more eco-conscious and energy-efficient future for AI systems."}100{"work_id": 4409363528, "title": "Intelligent nanophotonics: when machine learning sheds light", "abstract": "Abstract The synergistic development of nanophotonics and machine learning has inspired tremendous innovations in both fields in the past decade. In diverse photonics research, deep-learning methods using artificial neural networks become the key game changer that greatly facilitates rapid nanophotonics design and the versatile processing of optical information. Moreover, optical computing platforms that perform calculations through light propagation are receiving tremendous interest as next-generation machine-learning hardware with advantages in computing speed, energy efficiency, and parallelism. This review summarizes the current state-of-the-art nanophotonic devices enabled by machine learning and analyzes the longstanding challenges that must be overcome to make an impact on technology. We also discuss the opportunities of intelligent photonics in applications such as computational imaging/sensing and machine vision. The intersection of nanophotonics with deep learning holds tremendous implications for transformative technologies ranging from internet of things to smart health. Lastly, we provide our perspective on the pressing challenges in intelligent photonics that must be tackled to advance this field to the next level and the vast opportunities for multidisciplinary collaboration."}101{"work_id": 4390660405, "title": "The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data", "abstract": "With the intensification of global climate change, low-carbon energy has become a hot topic, and governments around the world are implementing corresponding policies to promote its use. This research first establishes a Multi-universe Quantum Harmony Search-Algorithm Dynamic Fuzzy System Ensemble (MUQHS-DMFSE) composite model for carbon emission prediction. This model combines the MUQHS algorithm with the DMFSE method by designing the workflow of the MUQHS algorithm, creating a DMFSE composite prediction model, introducing a sliding factor matrix, and using the MUQHS algorithm to search for the optimal sliding factors, thus obtaining optimized prediction values. In the research on low-carbon economic development, the research applies the Data Envelopment Analysis (DEA) method and establishes Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models to assess the technical efficiency, pure technical efficiency, and scale efficiency of decision-making units. This research also uses the BCC model to project the production frontier and calculate input redundancy and output gap rates, and evaluate low-carbon economic development. Through the establishment and application of these two models, the research achieves carbon emission prediction and low-carbon economic analysis, validating the feasibility of the research methodology. The results show that the composite model can effectively predict carbon emissions, with a Mean Absolute Percentage Error (MAPE) below 3.5% and Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) below 200 tons, demonstrating the feasibility and accuracy of the model. The research on low-carbon economic development in S Province based on the DEA method reveals the need for energy structure adjustment, clean and renewable energy promotion, control of carbon emissions, and optimization of industrial structure with a focus on developing the tertiary industry. Therefore, the use of artificial intelligence and big data analysis can provide more precise insights into the trends and patterns of low-carbon economic development, as well as more effective predictions of future energy demand and resource supply, offering high practical value and scientific significance."}102{"work_id": 7131638452, "title": "Digital product passports", "abstract": "Abstract Digital product passports (DPPs) will become mandatory in the European Union (EU) for a wide range of product categories under the Ecodesign for Sustainable Products Regulation (ESPR). DPPs are digital artifacts that capture and share essential product data across organizational boundaries. They have the potential to enhance transparency across value chains and ecosystems, thereby supporting data-driven decision-making throughout the entire product lifecycle and unlocking new opportunities for sustainable value creation. Despite growing momentum, the design, implementation, and management of DPPs are still evolving, resulting in a vast space of design options and ambiguity. This fundamentals paper reports on a multivocal literature review to advance our understanding of this emerging phenomenon from an information systems perspective. We position the DPP against adjacent concepts, define it, develop a conceptual framework, and present a demonstrative scenario that instantiates the conceptual framework\u2019s elements. The findings have implications for DPP research and practice and lay the groundwork for achieving both regulatory compliance and enabling additional sustainable pathways."}103{"work_id": 4411541921, "title": "From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate", "abstract": "As the climate crisis deepens, artificial intelligence (AI) has emerged as a contested force: some champion its potential to advance renewable energy, materials discovery, and large-scale emissions monitoring, while others underscore its growing carbon footprint, water consumption, and material resource demands. Much of this debate has concentrated on direct impacts -- energy and water usage in data centers, e-waste from frequent hardware upgrades -- without addressing the significant indirect effects. This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption. We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses. Rebound effects undermine the assumption that improved technical efficiency alone will ensure net reductions in environmental harm. Instead, the trajectory of AI's impact also hinges on business incentives and market logics, governance and policymaking, and broader social and cultural norms. We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions. We conclude with recommendations that address rebound effects and challenge the market-driven imperatives fueling uncontrolled AI growth. By broadening the analysis to include both direct and indirect consequences, we aim to inform a more comprehensive, evidence-based dialogue on AI's role in the climate crisis."}104{"work_id": 4362707075, "title": "Making AI Less \"Thirsty\": Uncovering and Addressing the Secret Water Footprint of AI Models", "abstract": "The growing carbon footprint of artificial intelligence (AI) has been undergoing public scrutiny. Nonetheless, the equally important water (withdrawal and consumption) footprint of AI has largely remained under the radar. For example, training the GPT-3 language model in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret. More critically, the global AI demand is projected to account for 4.2-6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4-6 Denmark or half of the United Kingdom. This is concerning, as freshwater scarcity has become one of the most pressing challenges. To respond to the global water challenges, AI can, and also must, take social responsibility and lead by example by addressing its own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI, and also discuss the unique spatial-temporal diversities of AI's runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI."}105{"work_id": 7122842330, "title": "Bridging technology and sustainability: examining the role of green AI adoption in Indian banking sector", "abstract": "The rapid integration of Artificial Intelligence (AI) in India\u2019s banking sector offers operational benefits but also raises sustainability challenges. This study focuses on \u201cGreen AI,\u201d defined as AI technologies optimized for energy efficiency and carbon conscious practices, by extending the Technology\u2013Organization\u2013Environment (TOE) and Technology Acceptance Model (TAM) frameworks with sustainability-linked factors. Data were collected from 412 mid- to senior-level professionals across six leading public and private banks, and Structural Equation Modeling (SEM) was employed to test the proposed hypotheses. Findings reveal that Banking Infrastructure ( \u03b2 = 0.419), Financial Investment ( \u03b2 = 0.401), and Competitive Pressure ( \u03b2 = 0.329) are the strongest predictors of Green AI adoption, while Regulatory Influence ( \u03b2 = 0.147), Perceived Usefulness ( \u03b2 = 0.129), and Perceived Ease of Use ( \u03b2 = 0.098) exert weaker but significant effects. Adoption of Green AI demonstrates a positive link to sustainability outcomes ( \u03b2 = 0.446), indicating its potential to convert structural readiness into measurable environmental gains. Although direct energy-consumption data were unavailable, perceptual measures provided valid proxies aligned with emerging-market studies. The results suggest that resource and market drivers outweigh attitudinal factors, offering actionable insights for infrastructure investment, regulatory refinement, and ESG integration, with implications for other emerging economies."}106{"work_id": 7127083763, "title": "Digital medicine\u2019s international race for regulatory sandboxes and voluntary alternative pathways picks up tempo", "abstract": "The last months of 2025 have brought major new policy initiatives and pilots for digital medicine and AI regulatory sandboxes from the EU, UK, and US regulators. The EU has seen a regulatory simplification package with the promotion of sandboxes for innovative technologies. The UK sees the start of an important second phase of the world-leading \u2018AI Airlock\u2019 regulatory sandbox program. TEMPO, a major voluntary alternative pathway regulatory innovation pilot for digital health in chronic disease has been announced by the US FDA. These frameworks bring flexible approaches to explore digital health technology innovation together with regulatory innovation. They will all substantially advance digital medicine, with the US TEMPO project being the most innovative, bringing sandboxing principles to the on-market phase."}107{"work_id": 4210357113, "title": "Opportunities for neuromorphic computing algorithms and applications", "abstract": null}108{"work_id": 4408612646, "title": "Microsatellite-based real-time quantum key distribution", "abstract": null}109{"work_id": 7149961640, "title": "A discrete memristive cyclic Hopfield neural network with multi-cavity-like attractors and application in secure communication", "abstract": null}110{"work_id": 7117963061, "title": "Toward sustainable data center operation: A review on existing infrastructures, integrated smart energy management frameworks, and future perspectives", "abstract": null}111{"work_id": 4414574650, "title": "Field and delay effects on synchronization in a multilayer memristive neuronal network with a hub", "abstract": null}112{"work_id": 4413052649, "title": "An attractor-controllable memristive Hopfield neural network and its application on voice encryption", "abstract": null}113{"work_id": 7141052859, "title": "A dual-neuron second-order memristor-based Hopfield neural network with controllable extreme multistability and its application in image encryption", "abstract": null}114{"work_id": 4409283612, "title": "Universal photonic artificial intelligence acceleration", "abstract": null}115{"work_id": 2944119451, "title": "All-optical spiking neurosynaptic networks with self-learning capabilities", "abstract": null}116{"work_id": 2892611063, "title": "Improving Big Data Centers Energy Efficiency: Traffic Based Model and Method", "abstract": null}117{"work_id": 4408847956, "title": "Barren plateaus in variational quantum computing", "abstract": null}118{"work_id": 7130682590, "title": "Extreme multistability in discrete memristive neuron maps and implications for dual-field applications", "abstract": null}119{"work_id": 7140216027, "title": "Discrete bidirectional memristive neural network-based hyperchaotic system and its FPGA implementation", "abstract": null}120{"work_id": 4403931219, "title": "BPF Instruction Set Architecture (ISA)", "abstract": null}121{"work_id": 7162190207, "title": "AI-driven mental health therapy: A comparative study of emotional clarity and stigma reduction across journaling, human-guided therapy, and AI-guided therapy", "abstract": null}122{"work_id": 4391530352, "title": "Data centers cooling: A critical review of techniques, challenges, and energy saving solutions", "abstract": null}123{"work_id": 3111721177, "title": "A Modern Primer on Processing in Memory", "abstract": null}124{"work_id": 7134974875, "title": "Neuromorphic Photonic Computing using Microring Resonator Arrays for Real-Time Edge AI Processing", "abstract": null}125{"work_id": 3003967059, "title": "Parallel convolutional processing using an integrated photonic tensor core", "abstract": null}126