1work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,ntitle,score,set,pi_review23099878876,,,Array programming with NumPy,RePEc: Research Papers in Economics,2020,article,computer_architecture,4,2343.0,5512.0,689.4179496808111,17096,889.1394,True,True,baseline,arrayprogrammingwithnumpy,689.4179496808111,landmark,True33046751266,10.1063/5.0014475,,Scalable molecular dynamics on CPU and GPU architectures with NAMD,The Journal of Chemical Physics,2020,article,computer_architecture,7,356.0,869.0,214.60681995758455,3144,304.3728,True,True,baseline,scalablemoleculardynamicsoncpuandgpuarchitectureswithnamd,214.60681995758455,landmark,True44394998532,10.1145/3620665.3640366,,PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation,ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS),2024,conference-paper,computer_architecture,6,434.0,690.0,204.38576349024112,690,218.9123,True,False,post,pytorch2fastermachinelearningthroughdynamicpythonbytecodetransformationandgraphcompilation,204.38576349024112,landmark,True54287393709,10.4230/lipics.ecrts.2026.17,,Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,computer_architecture,3,71.0,71.0,330.9365269461078,71,0.0,True,True,post,uncertaintyawareresourceallocationformultipathprogramswithinkernelpredictions,180.01839926555348,landmark,True64297813615,10.4230/lipics.ecrts.2024.13,,DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2024,article,computer_architecture,5,674.0,707.0,157.60297582979868,708,51.0217,True,True,post,deeptrustrtconfidentialdeepneuralinferencemeetsrealtime,157.60297582979868,landmark,True73111721177,10.1007/978-981-16-7487-7_7,,A Modern Primer on Processing in Memory,Computer architecture and design methodologies,2022,book-chapter,computer_architecture,2,90.0,125.0,139.4308943089431,168,112.4027,False,False,baseline,amodernprimeronprocessinginmemory,139.4308943089431,landmark,True82273211146,10.17615/nyex-d314,,The multiprocessor real-time scheduling of general task systems,Carolina Digital Repository (University of North Carolina at Chapel Hill),2019,dissertation,computer_architecture,4,19.0,20.0,137.14285714285714,37,,True,True,baseline,themultiprocessorrealtimeschedulingofgeneraltasksystems,137.14285714285714,landmark,True94286840061,10.7302/4622,,Algorithm-Architecture Co-Design for Domain-Specific Accelerators in Communication and Artificial Intelligence,Deep Blue (University of Michigan),2022,dissertation,computer_architecture,5,3.0,14.0,105.75490196078431,14,,True,True,baseline,algorithmarchitecturecodesignfordomainspecificacceleratorsincommunicationandartificialintelligence,105.75490196078431,landmark,True103016932526,10.1063/5.0005082,,Q uantum ESPRESSO toward the exascale,The Journal of Chemical Physics,2020,article,computer_architecture,4,175.0,425.0,104.9573055028463,2032,159.393,True,True,baseline,quantumespressotowardtheexascale,104.9573055028463,landmark,True114406650295,10.1145/3714983.3714987,,AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration,GetMobile Mobile Computing and Communications,2025,article,computer_architecture,9,234.0,234.0,76.51490775873349,234,83.4275,True,False,post,awqactivationawareweightquantizationforondevicellmcompressionandacceleration,102.2513468321514,landmark,True124401211704,10.1109/isca59077.2024.00019,,Splitwise: Efficient Generative LLM Inference Using Phase Splitting,ACM/IEEE International Symposium on Computer Architecture (ISCA),2024,conference-paper,computer_architecture,6,125.0,292.0,86.49368541905856,286,90.7376,True,False,post,splitwiseefficientgenerativellminferenceusingphasesplitting,86.49368541905856,landmark,True135673758,10.7275/4018988,,A Novel Reconfiguration Scheme in Quantum-Dot Cellular Automata for Energy Efficient Nanocomputing,ScholarWorks@UMassAmherst (University of Massachusetts Amherst),2023,dissertation,computer_architecture,4,4.0,4.0,86.3030303030303,4,,True,True,post,anovelreconfigurationschemeinquantumdotcellularautomataforenergyefficientnanocomputing,86.3030303030303,landmark,True146922095860,10.1184/r1/14642340,,Practical Mechanisms for Reducing Processor–Memory Data Movement in Modern Workloads,Figshare,2021,dissertation,computer_architecture,9,9.0,11.0,81.43283582089553,11,,True,True,baseline,practicalmechanismsforreducingprocessormemorydatamovementinmodernworkloads,81.43283582089553,landmark,True154386978266,10.7302/8543,,Accelerated Systems for Portable DNA Sequencing,Deep Blue (University of Michigan),2023,dissertation,computer_architecture,5,11.0,11.0,75.89999999999999,11,,True,True,post,acceleratedsystemsforportablednasequencing,75.89999999999999,landmark,True164393145114,10.1109/mm.2024.3373763,2403.14123,AI and Memory Wall,IEEE Micro,2024,article,computer_architecture,8,201.0,336.0,74.90042415673601,336,105.8227,True,True,post,aiandmemorywall,74.90042415673601,landmark,True171224547023,10.1201/9781315218823-12,,Model-Based Framework for Schedulability Analysis Using Uppaal 4.1,CRC Press eBooks,2018,book-chapter,computer_architecture,3,51.0,52.0,74.46337817638266,70,17.8443,True,True,baseline,modelbasedframeworkforschedulabilityanalysisusinguppaal41,74.46337817638266,landmark,True182972087877,10.1145/3320060,,Demystifying Parallel and Distributed Deep Learning,ACM Computing Surveys,2019,article,computer_architecture,6,159.0,263.0,74.14530533429881,577,81.2716,True,False,baseline,demystifyingparallelanddistributeddeeplearning,74.14530533429881,landmark,True192952332632,10.4230/oasics.asd.2020.1,,Towards a Reliable and Context-Based System Architecture for Autonomous Vehicles,OASIcs : OpenAccess Series in Informatics,2020,conference-paper,computer_architecture,3,395.0,516.0,73.13244814646819,797,52.3105,True,True,baseline,towardsareliableandcontextbasedsystemarchitectureforautonomousvehicles,73.13244814646819,landmark,True202921153466,10.48550/arxiv.1903.05714,1903.05714,Basic Performance Measurements of the Intel Optane DC Persistent Memory Module,arXiv (Cornell University),2019,preprint,computer_architecture,6,108.0,175.0,71.88172043010752,191,,True,True,baseline,basicperformancemeasurementsoftheinteloptanedcpersistentmemorymodule,71.88172043010752,landmark,True214403931219,10.17487/rfc9669,,BPF Instruction Set Architecture (ISA),,2024,report,computer_architecture,4,4.0,9.0,66.0,9,,False,False,post,bpfinstructionsetarchitectureisa,66.0,landmark,True222931743911,10.1109/hpca.2019.00048,,Machine Learning at Facebook: Understanding Inference at the Edge,Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings,2019,conference-paper,computer_architecture,7,108.0,212.0,64.45974888151248,439,66.0809,True,False,baseline,machinelearningatfacebookunderstandinginferenceattheedge,64.45974888151248,landmark,True231983572666,10.1016/c2011-0-06993-4,,The Art of Multiprocessor Programming,Elsevier eBooks,2020,book,computer_architecture,2,852.0,864.0,63.65562913907285,1093,24.414,False,False,baseline,theartofmultiprocessorprogramming,63.65562913907285,landmark,True2475685577,10.7275/2207079,,Electromagnetic Side-Channel Analysis for Hardware and Software Watermarking,Scholarworks (University of Massachusetts Amherst),2023,dissertation,computer_architecture,3,9.0,9.0,62.099999999999994,9,,True,True,post,electromagneticsidechannelanalysisforhardwareandsoftwarewatermarking,62.099999999999994,landmark,True254410153295,10.1109/tcad.2025.3567878,,Bursting Firings in Memristive Hopfield Neural Network With Image Encryption and Hardware Implementation,IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2025,article,computer_architecture,4,142.0,142.0,32.564312668463614,142,32.3565,True,False,post,burstingfiringsinmemristivehopfieldneuralnetworkwithimageencryptionandhardwareimplementation,62.04996260754486,landmark,True264380874786,10.1145/3579371.3589350,2304.01433,TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings,Proceedings - International Symposium on Computer Architecture/Proceedings,2023,conference-paper,computer_architecture,10,167.0,388.0,61.6547248352945,508,94.1928,True,True,post,tpuv4anopticallyreconfigurablesupercomputerformachinelearningwithhardwaresupportforembeddings,61.6547248352945,landmark,True272156094048,10.1145/3501714.3501727,,Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach,ACM eBooks,2022,book-chapter,ai_accelerators,2,495.0,513.0,274.919235405545,727,46.4544,True,False,baseline,reverendbayesoninferenceenginesadistributedhierarchicalapproach,274.919235405545,landmark,True283003967059,10.1038/s41586-020-03070-1,2002.00281,Parallel convolutional processing using an integrated photonic tensor core,Nature,2021,article,ai_accelerators,2,503.0,830.0,96.73020860110631,1874,105.2419,False,True,baseline,parallelconvolutionalprocessingusinganintegratedphotonictensorcore,96.73020860110631,landmark,True297130682590,10.1016/j.vlsi.2026.102688,,Extreme multistability in discrete memristive neuron maps and implications for dual-field applications,Integration,2026,article,ai_accelerators,2,37.0,37.0,38.12608695652174,37,127.1161,False,False,post,extrememultistabilityindiscretememristiveneuronmapsandimplicationsfordualfieldapplications,93.81240525106308,landmark,True304409283612,10.1038/s41586-025-08854-x,,Universal photonic artificial intelligence acceleration,Nature,2025,article,ai_accelerators,1,165.0,165.0,56.20713647719771,165,68.6604,False,False,post,universalphotonicartificialintelligenceacceleration,72.1003086636965,landmark,True312783525259,10.1109/mm.2018.112130359,,Loihi: A Neuromorphic Manycore Processor with On-Chip Learning,IEEE Micro,2018,article,ai_accelerators,4,327.0,745.0,64.97395387432005,3897,115.1562,True,False,baseline,loihianeuromorphicmanycoreprocessorwithonchiplearning,64.97395387432005,landmark,True327131231908,10.1109/tcyb.2026.3664460,,Reconfigurable Multiscroll Memristive Neural Network With Application to Telemedicine Privacy Protection,IEEE Transactions on Cybernetics,2026,article,ai_accelerators,5,25.0,25.0,67.00916730328495,25,77.9076,True,False,post,reconfigurablemultiscrollmemristiveneuralnetworkwithapplicationtotelemedicineprivacyprotection,63.38676030477235,landmark,True337135102590,10.1109/tii.2026.3668794,,Biologically Plausible Memristive Decision-Making Circuit for Adaptive Control in Industrial Autonomous Navigation,IEEE Transactions on Industrial Informatics,2026,article,ai_accelerators,2,24.0,24.0,35.86027014438752,24,41.1527,True,False,post,biologicallyplausiblememristivedecisionmakingcircuitforadaptivecontrolinindustrialautonomousnavigation,60.851289892581455,landmark,True347134974875,10.1109/icears67481.2026.11416637,,Neuromorphic Photonic Computing using Microring Resonator Arrays for Real-Time Edge AI Processing,International Conference on Electronics and Renewable Systems (ICEARS),2026,conference-paper,ai_accelerators,3,24.0,24.0,184.20139553974553,24,221.8777,False,False,post,neuromorphicphotoniccomputingusingmicroringresonatorarraysforrealtimeedgeaiprocessing,60.851289892581455,landmark,True352944119451,10.1038/s41586-019-1157-8,2102.09360,All-optical spiking neurosynaptic networks with self-learning capabilities,Nature,2019,article,ai_accelerators,3,162.0,384.0,58.38929375910958,1529,75.8628,False,True,baseline,allopticalspikingneurosynapticnetworkswithselflearningcapabilities,58.38929375910958,landmark,True367128511721,10.1109/tce.2026.3663359,,A Memristor-Based Neural Network Circuit With Classical Conditioning and Fear Generalization,IEEE Transactions on Consumer Electronics,2026,article,ai_accelerators,3,23.0,23.0,34.366092221704704,23,39.438,True,False,post,amemristorbasedneuralnetworkcircuitwithclassicalconditioningandfeargeneralization,58.315819480390566,landmark,True374210357113,10.1038/s43588-021-00184-y,,Opportunities for neuromorphic computing algorithms and applications,Nature Computational Science,2022,article,ai_accelerators,2,224.0,537.0,42.17948197363679,1107,60.0929,False,True,baseline,opportunitiesforneuromorphiccomputingalgorithmsandapplications,42.17948197363679,landmark,True384394727812,10.1126/science.adl1203,,Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence,Science,2024,article,ai_accelerators,4,198.0,308.0,38.256050750798984,308,45.8367,True,False,post,largescalephotonicchiplettaichiempowers160topswartificialgeneralintelligence,38.256050750798984,landmark,True397141052859,10.1016/j.vlsi.2026.102731,,A dual-neuron second-order memristor-based Hopfield neural network with controllable extreme multistability and its application in image encryption,Integration,2026,article,ai_accelerators,4,15.0,15.0,40.20550038197097,15,46.7446,False,False,post,adualneuronsecondordermemristorbasedhopfieldneuralnetworkwithcontrollableextrememultistabilityanditsapplicationinimageencryption,38.03205618286341,landmark,True407149961640,10.1016/j.neunet.2026.108953,,A discrete memristive cyclic Hopfield neural network with multi-cavity-like attractors and application in secure communication,Neural Networks,2026,article,ai_accelerators,3,14.0,14.0,14.426086956521738,14,48.098,False,False,post,adiscretememristivecyclichopfieldneuralnetworkwithmulticavitylikeattractorsandapplicationinsecurecommunication,35.49658577067252,landmark,True414409363528,10.1186/s43593-025-00085-x,,Intelligent nanophotonics: when machine learning sheds light,eLight,2025,article,ai_accelerators,5,77.0,77.0,26.229997022692263,77,32.0415,True,True,post,intelligentnanophotonicswhenmachinelearningshedslight,33.64681070972503,landmark,True427140216027,10.1016/j.neunet.2026.108887,,Discrete bidirectional memristive neural network-based hyperchaotic system and its FPGA implementation,Neural Networks,2026,article,ai_accelerators,3,13.0,13.0,13.395652173913042,13,44.6624,False,False,post,discretebidirectionalmemristiveneuralnetworkbasedhyperchaoticsystemanditsfpgaimplementation,32.96111535848162,landmark,True434402694963,10.1038/s41377-024-01590-3,,Optical neural networks: progress and challenges,Light Science & Applications,2024,article,ai_accelerators,4,135.0,263.0,32.666692686558875,263,39.1398,True,True,post,opticalneuralnetworksprogressandchallenges,32.666692686558875,landmark,True444210305137,10.1038/s41377-022-00717-8,,Photonic matrix multiplication lights up photonic accelerator and beyond,Light Science & Applications,2022,article,ai_accelerators,3,161.0,306.0,31.53012133083438,563,38.2097,True,True,baseline,photonicmatrixmultiplicationlightsupphotonicacceleratorandbeyond,31.53012133083438,landmark,True454414574650,10.1016/j.chaos.2025.117292,,Field and delay effects on synchronization in a multilayer memristive neuronal network with a hub,Chaos Solitons & Fractals,2025,article,ai_accelerators,1,71.0,71.0,27.006304798493694,71,31.2974,False,False,post,fieldanddelayeffectsonsynchronizationinamultilayermemristiveneuronalnetworkwithahub,31.02498130377243,landmark,True462799210008,10.48550/arxiv.1804.09848,1804.09848,Memristor Crossbars with 4.5 Terabits-per-Inch-Square Density and Two Nanometer Dimension,arXiv (Cornell University),2018,preprint,ai_accelerators,2,38.0,109.0,30.82828282828283,154,,True,True,baseline,memristorcrossbarswith45terabitsperinchsquaredensityandtwonanometerdimension,30.82828282828283,landmark,True477130433466,10.1109/tcsi.2026.3663432,,A Second-Order Memristor Method to Construct Memristive Neural Networks With Multi-Butterfly and Multi-Scroll Dynamics,IEEE Transactions on Circuits and Systems I Regular Papers,2026,article,ai_accelerators,4,12.0,12.0,17.93013507219376,12,20.5764,True,False,post,asecondordermemristormethodtoconstructmemristiveneuralnetworkswithmultibutterflyandmultiscrolldynamics,30.425644946290728,landmark,True487130516837,10.1142/s0218127426500914,,Gaussian-Function-Driven Chaotic Memristive Dynamics with Application to Image Encryption,International Journal of Bifurcation and Chaos,2026,article,ai_accelerators,3,12.0,12.0,32.16440030557678,12,37.3956,True,False,post,gaussianfunctiondrivenchaoticmemristivedynamicswithapplicationtoimageencryption,30.425644946290728,landmark,True493121141908,10.1038/s41467-020-20719-7,,An optical neural chip for implementing complex-valued neural network,Nature Communications,2021,article,ai_accelerators,4,124.0,260.0,30.301029200346555,666,36.6979,True,True,baseline,anopticalneuralchipforimplementingcomplexvaluedneuralnetwork,30.301029200346555,landmark,True504360605906,10.1109/isscc42615.2023.10067837,,"33.1 A 16nm 32Mb Embedded STT-MRAM with a 6ns Read-Access Time, a 1M-Cycle Write Endurance, 20-Year Retention at 150°C and MTJ-OTP Solutions for Magnetic Immunity",Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference,2023,conference-paper,ai_accelerators,2,27.0,53.0,29.22718446601942,69,119.8281,True,False,post,331a16nm32mbembeddedsttmramwitha6nsreadaccesstimea1mcyclewriteendurance20yearretentionat150candmtjotpsolutionsformagneticimmunity,29.22718446601942,landmark,True514413052649,10.1016/j.vlsi.2025.102500,,An attractor-controllable memristive Hopfield neural network and its application on voice encryption,Integration,2025,article,ai_accelerators,4,66.0,66.0,14.175875486381322,66,28.5628,False,False,post,anattractorcontrollablememristivehopfieldneuralnetworkanditsapplicationonvoiceencryption,28.840123465478598,landmark,True522916516729,10.5555/3323298.3323317,,SLM-DB: Single-Level Key-Value Store with Persistent Memory,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2019,dissertation,operating_systems,7,19.0,42.0,328.8494623655914,75,,True,True,baseline,slmdbsinglelevelkeyvaluestorewithpersistentmemory,328.8494623655914,landmark,True534295093682,10.5281/zenodo.19901496,,fast16 as a Second-Order Subversion Attack,,2026,preprint,operating_systems,4,28.0,28.0,1089.5438596491229,28,,True,True,post,fast16asasecondordersubversionattack,70.99317154134503,landmark,True5425727798,10.48550/arxiv.1903.01955,1903.01955,The Lustre Storage Architecture,arXiv (Cornell University),2019,preprint,operating_systems,3,96.0,112.0,46.00430107526881,206,,True,True,baseline,thelustrestoragearchitecture,46.00430107526881,landmark,True552930508541,10.4230/oasics.microservices.2020-2022.5,1902.03383,Cloud Programming Simplified: A Berkeley View on Serverless Computing,arXiv (Cornell University),2019,preprint,operating_systems,6,103.0,195.0,43.97867397390838,409,,True,True,baseline,cloudprogrammingsimplifiedaberkeleyviewonserverlesscomputing,43.97867397390838,landmark,True564404401018,10.1145/3694715.3695964,2312.12456,PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU,ACM SIGOPS Symposium on Operating Systems Principles (SOSP),2024,conference-paper,operating_systems,10,64.0,133.0,39.39609644087256,133,42.1961,True,True,post,powerinferfastlargelanguagemodelservingwithaconsumergradegpu,39.39609644087256,landmark,True573182301081,,,Zico: Efficient {GPU} Memory Sharing for Concurrent {DNN} Training,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2021,dissertation,operating_systems,5,0.0,5.0,37.01492537313433,8,,True,False,baseline,zicoefficientgpumemorysharingforconcurrentdnntraining,37.01492537313433,landmark,True587130515337,10.1186/s12912-026-04420-6,,Artificial intelligence literacy and academic resilience in undergraduate nursing students: the mediating role of self-efficacy and artificial intelligence anxiety,BMC Nursing,2026,article,operating_systems,1,13.0,13.0,92.3590525632706,13,118.9868,True,True,post,artificialintelligenceliteracyandacademicresilienceinundergraduatenursingstudentsthemediatingroleofselfefficacyandartificialintelligenceanxiety,32.96111535848162,landmark,True592975712713,10.1145/3341301.3359642,,A generic communication scheduler for distributed DNN training acceleration,,2019,conference-paper,operating_systems,8,53.0,119.0,31.997431506849313,321,32.6252,True,False,baseline,agenericcommunicationschedulerfordistributeddnntrainingacceleration,31.997431506849313,landmark,True604384697207,10.22215/etd/2023-15552,,Methods and Techniques for Inter-cloud Data Transfers,,2023,dissertation,operating_systems,4,1.0,1.0,28.864864864864863,1,,True,True,post,methodsandtechniquesforinterclouddatatransfers,28.864864864864863,landmark,True614394944658,10.1145/3627703.3629578,,"Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications",,2024,conference-paper,operating_systems,7,56.0,97.0,28.73249138920781,97,30.7746,True,False,post,orioninterferenceawarefinegrainedgpusharingformlapplications,28.73249138920781,landmark,True627162190207,10.1016/j.dss.2026.114696,,"AI-driven mental health therapy: A comparative study of emotional clarity and stigma reduction across journaling, human-guided therapy, and AI-guided therapy",Decision Support Systems,2026,article,operating_systems,1,10.0,10.0,5.225225225225225,10,63.1822,False,False,post,aidrivenmentalhealththerapyacomparativestudyofemotionalclarityandstigmareductionacrossjournalinghumanguidedtherapyandaiguidedtherapy,25.35470412190894,landmark,True634295682935,,,CodaLab Competitions An Open Source Platform to Organize Scientific Challenges,HAL (Le Centre pour la Communication Scientifique Directe),2022,report,operating_systems,4,28.0,68.0,22.289935364727608,75,,True,True,baseline,codalabcompetitionsanopensourceplatformtoorganizescientificchallenges,22.289935364727608,landmark,True643152809665,10.1145/3445814.3446701,,"Nightcore: efficient and scalable serverless computing for latency-sensitive, interactive microservices",,2021,conference-paper,operating_systems,5,33.0,78.0,21.88714698058747,215,30.1787,True,True,baseline,nightcoreefficientandscalableserverlesscomputingforlatencysensitiveinteractivemicroservices,21.88714698058747,landmark,True653010663313,10.1145/3373376.3378515,,FlatStore: An Efficient Log-Structured Key-Value Storage Engine for Persistent Memory,International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS),2020,conference-paper,operating_systems,5,38.0,70.0,21.67711021009952,165,23.8709,True,False,baseline,flatstoreanefficientlogstructuredkeyvaluestorageengineforpersistentmemory,21.67711021009952,landmark,True663092732687,,,PENGEMBANGAN SISTEM INFORMASI PENERIMAANMAHASISWA BARU DENGAN METODE BUSINESSPROCESS AUTOMATION (STUDI KASUS: INSTITUTTEKNOLOGI KALIMANTAN),,2019,dissertation,operating_systems,1,0.0,1.0,21.373831775700936,1,,True,False,baseline,pengembangansisteminformasipenerimaanmahasiswabarudenganmetodebusinessprocessautomationstudikasusinstitutteknologikalimantan,21.373831775700936,landmark,True673138262672,10.5445/ir/1000097700,,SimuBoost: Scalable Parallelization of Functional System Simulation,Repository KITopen (Karlsruhe Institute of Technology),2019,dissertation,operating_systems,5,3.0,3.0,20.57142857142857,3,,True,True,baseline,simuboostscalableparallelizationoffunctionalsystemsimulation,20.57142857142857,landmark,True684366587887,10.3390/su15086917,,Promising Emerging Technologies for Teaching and Learning: Recent Developments and Future Challenges,Sustainability,2023,article,operating_systems,1,44.0,83.0,19.453189395067895,108,24.9088,True,True,post,promisingemergingtechnologiesforteachingandlearningrecentdevelopmentsandfuturechallenges,19.453189395067895,landmark,True693134501457,,,KLASIFIKASI INDEKS PEMBANGUNAN MANUSIA KABUPATEN/KOTA DI INDONESIA MENGGUNAKANMETODE RANDOM FOREST,,2020,dissertation,operating_systems,1,1.0,2.0,18.809744779582367,5,,True,False,baseline,klasifikasiindekspembangunanmanusiakabupatenkotadiindonesiamenggunakanmetoderandomforest,18.809744779582367,landmark,True704385215131,10.23919/vlsitechnologyandcir57934.2023.10185294,,QLC Programmable 3D Ferroelectric NAND Flash Memory by Memory Window Expansion using Cell Stack Engineering,IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits),2023,conference-paper,operating_systems,1,28.0,55.0,18.331575035365766,70,35.7909,True,False,post,qlcprogrammable3dferroelectricnandflashmemorybymemorywindowexpansionusingcellstackengineering,18.331575035365766,landmark,True712807211499,,,Towards Efficient and Flexible Object Storage Using Resource and Functional Partitioning,VTechWorks (Virginia Tech),2018,dissertation,operating_systems,4,2.0,2.0,17.98848368522073,3,,True,False,baseline,towardsefficientandflexibleobjectstorageusingresourceandfunctionalpartitioning,17.98848368522073,landmark,True724407217670,10.1145/3669940.3707256,2405.04437,vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention,ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS),2025,conference-paper,operating_systems,7,40.0,40.0,34.965113033770585,40,35.3801,True,True,post,vattentiondynamicmemorymanagementforservingllmswithoutpagedattention,17.478862706350665,landmark,True731653864426,10.1007/978-981-13-0599-3,,Inside Solid State Drives (SSDs),Springer series in advanced microelectronics,2018,book,operating_systems,2,51.0,57.0,16.97693817468106,82,6.0183,False,False,baseline,insidesolidstatedrivesssds,16.97693817468106,landmark,True742798337732,10.1145/3190508.3190524,,Reducing DRAM footprint with NVM in Facebook,,2018,conference-paper,operating_systems,5,27.0,55.0,16.76435545739858,124,15.8731,True,True,baseline,reducingdramfootprintwithnvminfacebook,16.76435545739858,landmark,True754396654316,10.1002/sd.3021,,"Digital financial inclusion, financial stability, and sustainable development: Evidence from a quantile‐on‐quantile regression and wavelet coherence",Sustainable Development,2024,article,operating_systems,1,38.0,53.0,15.956977383908523,53,20.2822,True,True,post,digitalfinancialinclusionfinancialstabilityandsustainabledevelopmentevidencefromaquantileonquantileregressionandwaveletcoherence,15.956977383908523,landmark,True763181035463,,,{ZNS}: Avoiding the Block Interface Tax for Flash-based SSDs,USENIX Annual Technical Conference,2021,conference-paper,operating_systems,4,26.0,46.0,15.661939184411095,82,17.1134,False,False,baseline,znsavoidingtheblockinterfacetaxforflashbasedssds,15.661939184411095,landmark,True772982169647,10.1038/s41586-019-1666-5,,Quantum supremacy using a programmable superconducting processor,Nature,2019,article,quantum_computing,4,941.0,2260.0,343.6453226447595,7695,402.3374,True,True,baseline,quantumsupremacyusingaprogrammablesuperconductingprocessor,343.6453226447595,landmark,True782781738013,10.22331/q-2018-08-06-79,,Quantum Computing in the NISQ era and beyond,Quantum,2018,article,quantum_computing,2,386.0,1026.0,193.65188671645743,8559,285.8725,True,True,baseline,quantumcomputinginthenisqeraandbeyond,193.65188671645743,landmark,True794396986598,10.48550/arxiv.2405.08810,2405.08810,Quantum computing with Qiskit,arXiv (Cornell University),2024,preprint,quantum_computing,2,148.0,208.0,181.45149859248218,208,,True,True,post,quantumcomputingwithqiskit,181.45149859248218,landmark,True804408847956,10.1038/s42254-025-00813-9,2405.00781,Barren plateaus in variational quantum computing,Nature Reviews Physics,2025,review,quantum_computing,2,257.0,257.0,31.907270486752928,257,34.4334,False,True,post,barrenplateausinvariationalquantumcomputing,112.30169288830302,landmark,True813107463944,10.1126/science.abe8770,,Quantum computational advantage using photons,Science,2020,article,quantum_computing,3,294.0,769.0,96.1833097432046,2267,120.9449,True,True,baseline,quantumcomputationaladvantageusingphotons,96.1833097432046,landmark,True824213212652,10.1103/revmodphys.94.015004,,Noisy intermediate-scale quantum algorithms,Reviews of Modern Physics,2022,article,quantum_computing,2,509.0,908.0,93.55996787058045,1718,108.1665,True,True,baseline,noisyintermediatescalequantumalgorithms,93.55996787058045,landmark,True834405183524,10.1038/s41586-024-08449-y,,Quantum error correction below the surface code threshold,Nature,2024,article,quantum_computing,3,394.0,751.0,93.2801756943183,751,111.7642,True,True,post,quantumerrorcorrectionbelowthesurfacecodethreshold,93.2801756943183,landmark,True844386285839,10.3390/admsci13090196,,Managing the Strategic Transformation of Higher Education through Artificial Intelligence,Administrative Sciences,2023,article,quantum_computing,2,165.0,380.0,89.06279482079277,543,127.3637,True,True,post,managingthestrategictransformationofhighereducationthroughartificialintelligence,89.06279482079277,landmark,True854407158326,10.1038/s41586-024-08404-x,,Distributed quantum computing across an optical network link,Nature,2025,article,quantum_computing,5,192.0,192.0,65.40466790073914,192,79.0635,True,True,post,distributedquantumcomputingacrossanopticalnetworklink,83.8985409904832,landmark,True863156168805,10.15488/11476,,Ultra-low vibration closed-cycle cryogenic surface-electrode ion trap apparatus,Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover),2021,dissertation,quantum_computing,2,10.0,18.0,81.87032967032967,25,,True,True,baseline,ultralowvibrationclosedcyclecryogenicsurfaceelectrodeiontrapapparatus,81.87032967032967,landmark,True874321610982,10.1038/s41586-022-05434-1,,Suppressing quantum errors by scaling a surface code logical qubit,Nature,2023,article,quantum_computing,1,462.0,879.0,80.69640995309555,1083,94.0878,True,True,post,suppressingquantumerrorsbyscalingasurfacecodelogicalqubit,80.69640995309555,landmark,True884406693713,10.1038/s41586-024-08406-9,,Scaling and networking a modular photonic quantum computer,Nature,2025,article,quantum_computing,4,178.0,178.0,60.63557753297692,178,73.6539,True,True,post,scalingandnetworkingamodularphotonicquantumcomputer,77.78093904326046,landmark,True892788945937,10.37686/qrl.v1i2.80,,Classification with Quantum Neural Networks on Near Term Processors,,2020,report,quantum_computing,1,188.0,222.0,77.25930521091811,332,,True,True,baseline,classificationwithquantumneuralnetworksonneartermprocessors,77.25930521091811,landmark,True903174122836,10.1201/9781003358817-7,,Quantum Computing 40 Years Later,CRC Press eBooks,2023,book-chapter,quantum_computing,3,84.0,139.0,76.8491680085883,123,68.2265,True,True,baseline,quantumcomputing40yearslater,76.8491680085883,landmark,True914389373471,10.1038/s41586-023-06927-3,,Logical quantum processor based on reconfigurable atom arrays,Nature,2023,article,quantum_computing,3,242.0,826.0,75.83075611064497,1099,96.5476,True,True,post,logicalquantumprocessorbasedonreconfigurableatomarrays,75.83075611064497,landmark,True924380627387,10.1038/s41586-023-06096-3,,Evidence for the utility of quantum computing before fault tolerance,Nature,2023,article,quantum_computing,3,422.0,821.0,75.37173216324398,1033,90.7495,True,True,post,evidencefortheutilityofquantumcomputingbeforefaulttolerance,75.37173216324398,landmark,True934294786246,,,Time-optimal holonomic quantum computation,KTH Publication Database DiVA (KTH Royal Institute of Technology),2022,dissertation,quantum_computing,1,7.0,10.0,74.89035087719299,13,,True,True,baseline,timeoptimalholonomicquantumcomputation,74.89035087719299,landmark,True944407091026,10.1016/s2589-7500(24)00267-x,,"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",The Lancet Digital Health,2025,article,quantum_computing,1,164.0,164.0,55.86648716521469,164,67.8282,True,True,post,screeningperformanceandcharacteristicsofbreastcancerdetectedinthemammographyscreeningwithartificialintelligencetrialmasaiarandomisedcontrolledparallelgroupnoninferioritysingleblindedscreeningaccuracystudy,71.66333709603774,landmark,True952798434869,10.1038/s41586-019-0980-2,,Supervised learning with quantum-enhanced feature spaces,Nature,2019,article,quantum_computing,1,182.0,449.0,68.27289817145886,2519,80.6977,False,True,baseline,supervisedlearningwithquantumenhancedfeaturespaces,68.27289817145886,landmark,True964409126690,10.1007/s11227-025-07047-7,2410.00916,"IBM quantum computers: evolution, performance, and future directions",The Journal of Supercomputing,2025,article,quantum_computing,4,151.0,151.0,51.438046109435476,151,61.5863,True,True,post,ibmquantumcomputersevolutionperformanceandfuturedirections,65.98270671647376,landmark,True974408612646,10.1038/s41586-025-08739-z,,Microsatellite-based real-time quantum key distribution,Nature,2025,article,quantum_computing,3,151.0,151.0,51.438046109435476,151,62.4186,False,True,post,microsatellitebasedrealtimequantumkeydistribution,65.98270671647376,landmark,True981868882999,10.82444/warw.33678349,,Formal verification techniques using quantum process calculus,Warwick Research Archive Portal (University of Warwick),2026,dissertation,quantum_computing,5,26.0,26.0,795.7491039426524,27,,True,True,post,formalverificationtechniquesusingquantumprocesscalculus,65.92223071696324,landmark,True992482126025,10.1038/s41567-018-0124-x,,Characterizing quantum supremacy in near-term devices,Nature Physics,2018,article,quantum_computing,1,219.0,345.0,65.11686249237604,1156,65.4918,False,True,baseline,characterizingquantumsupremacyinneartermdevices,65.11686249237604,landmark,True1002950805899,10.1063/1.5089550,,A quantum engineer's guide to superconducting qubits,Applied Physics Reviews,2019,article,quantum_computing,1,162.0,416.0,63.255068239035374,2004,77.9349,True,True,baseline,aquantumengineersguidetosuperconductingqubits,63.255068239035374,landmark,True1013173899054,10.1103/physrevlett.127.180501,,Strong Quantum Computational Advantage Using a Superconducting Quantum Processor,Physical Review Letters,2021,article,quantum_computing,4,274.0,540.0,62.93290680071977,1060,68.1911,True,True,baseline,strongquantumcomputationaladvantageusingasuperconductingquantumprocessor,62.93290680071977,landmark,True1024362707075,10.48550/arxiv.2304.03271,2304.03271,"Making AI Less ""Thirsty"": Uncovering and Addressing the Secret Water Footprint of AI Models",arXiv (Cornell University),2023,preprint,sustainable_computing,2,66.0,133.0,93.25822253324003,162,,True,True,post,makingailessthirstyuncoveringandaddressingthesecretwaterfootprintofaimodels,93.25822253324003,landmark,True1032963840171,10.1109/comst.2018.2812301,1802.09345,"Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives",IEEE Communications Surveys & Tutorials,2018,article,sustainable_computing,2,95.0,155.0,82.1805490963324,613,105.2524,True,True,baseline,informationandcommunicationstechnologiesforsustainabledevelopmentgoalsstateoftheartneedsandperspectives,82.1805490963324,landmark,True1047117963061,10.1016/j.rser.2025.116664,,"Toward sustainable data center operation: A review on existing infrastructures, integrated smart energy management frameworks, and future perspectives",Renewable and Sustainable Energy Reviews,2026,article,sustainable_computing,3,30.0,30.0,213.13627514600907,30,274.5849,False,False,post,towardsustainabledatacenteroperationareviewonexistinginfrastructuresintegratedsmartenergymanagementframeworksandfutureperspectives,76.06411236572681,landmark,True1054411368489,10.1145/3724499,,Making AI Less 'Thirsty',Communications of the ACM,2025,article,sustainable_computing,2,163.0,163.0,37.38016172506738,163,37.1417,True,True,post,makingailessthirsty,71.22636552837896,landmark,True1064403458785,10.1186/s41239-024-00490-1,,"Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs",International Journal of Educational Technology in Higher Education,2024,article,sustainable_computing,1,72.0,206.0,62.021459265757656,206,78.8326,True,True,post,artificialintelligenceinhighereducationexploringfacultyuseselfefficacydistinctprofilesandprofessionaldevelopmentneeds,62.021459265757656,landmark,True1072892611063,10.1007/978-3-030-00253-4_8,,Improving Big Data Centers Energy Efficiency: Traffic Based Model and Method,"Studies in systems, decision and control",2018,book-chapter,sustainable_computing,3,28.0,50.0,58.75865606565786,88,58.8035,False,False,baseline,improvingbigdatacentersenergyefficiencytrafficbasedmodelandmethod,58.75865606565786,landmark,True1087140797778,10.1109/tetci.2026.3671111,,Carbon Footprint Efficiency Ratio: A Unified Indicator for Green Evaluation of Evolutionary Algorithms,IEEE Transactions on Emerging Topics in Computational Intelligence,2026,article,sustainable_computing,5,20.0,20.0,29.883558453656264,20,34.2939,True,False,post,carbonfootprintefficiencyratioaunifiedindicatorforgreenevaluationofevolutionaryalgorithms,50.70940824381788,landmark,True1094308245305,10.48550/arxiv.2211.02001,2211.02001,"Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model",arXiv (Cornell University),2022,preprint,sustainable_computing,2,24.0,94.0,49.0919881305638,178,,True,True,baseline,estimatingthecarbonfootprintofblooma176bparameterlanguagemodel,49.0919881305638,landmark,True1104415057924,10.1016/j.ynexs.2026.100162,2509.07218,Electricity demand and grid impacts of AI data centers: Challenges and prospects,Nexus,2026,article,sustainable_computing,3,18.0,18.0,26.89520260829064,18,29.1498,True,True,post,electricitydemandandgridimpactsofaidatacenterschallengesandprospects,45.63846741943609,landmark,True1114399913870,10.1016/j.neucom.2024.128096,,A review of green artificial intelligence: Towards a more sustainable future,Neurocomputing,2024,article,sustainable_computing,3,244.0,466.0,42.08809549196657,466,47.9661,True,True,post,areviewofgreenartificialintelligencetowardsamoresustainablefuture,42.08809549196657,landmark,True1124391530352,10.1016/j.ijrefrig.2024.02.007,,"Data centers cooling: A critical review of techniques, challenges, and energy saving solutions",International Journal of Refrigeration,2024,article,sustainable_computing,3,82.0,173.0,38.56480172355753,173,54.4861,False,False,post,datacenterscoolingacriticalreviewoftechniqueschallengesandenergysavingsolutions,38.56480172355753,landmark,True1134385481791,10.1145/3604930.3605705,,Reducing the Carbon Impact of Generative AI Inference (today and in 2035),,2023,conference-paper,sustainable_computing,5,44.0,110.0,36.66315007073153,127,64.935,True,True,post,reducingthecarbonimpactofgenerativeaiinferencetodayandin2035,36.66315007073153,landmark,True1147129692983,10.1109/tnse.2026.3665604,,Spatio-Temporal Coordinated Operation Strategy of Data Centers Considering Virtual Storage System via Two-Stage Distributionally Robust Optimization,IEEE Transactions on Network Science and Engineering,2026,article,sustainable_computing,4,13.0,13.0,92.3590525632706,13,118.9868,True,True,post,spatiotemporalcoordinatedoperationstrategyofdatacentersconsideringvirtualstoragesystemviatwostagedistributionallyrobustoptimization,32.96111535848162,landmark,True1157128511066,10.1016/j.ssaho.2026.102560,,"The impacts of globalization, institutional quality, and digital technology innovation on OECD countries sustainable digital economic growth",Social Sciences & Humanities Open,2026,article,sustainable_computing,1,13.0,13.0,92.3590525632706,13,118.9868,True,True,post,theimpactsofglobalizationinstitutionalqualityanddigitaltechnologyinnovationonoecdcountriessustainabledigitaleconomicgrowth,32.96111535848162,landmark,True1164390660405,10.1109/access.2024.3351468,,The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data,IEEE Access,2024,article,sustainable_computing,1,167.0,193.0,32.37439457539554,193,69.0898,True,True,post,theoptimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata,32.37439457539554,landmark,True1174283157527,10.1145/3531146.3533234,2206.05229,Measuring the Carbon Intensity of AI in Cloud Instances,"ACM Conference on Fairness, Accountability, and Transparency (FAccT)",2022,conference-paper,sustainable_computing,4,47.0,104.0,31.08684263550272,234,73.2411,True,True,baseline,measuringthecarbonintensityofaiincloudinstances,31.08684263550272,landmark,True1184407614581,10.1007/s11528-025-01055-3,,Examining Teaching Competencies and Challenges While Integrating Artificial Intelligence in Higher Education,TechTrends,2025,article,sustainable_computing,1,71.0,71.0,61.64226839442977,71,77.5671,True,True,post,examiningteachingcompetenciesandchallengeswhileintegratingartificialintelligenceinhighereducation,31.02498130377243,landmark,True1197115707641,10.1016/j.patter.2025.101430,,The carbon and water footprints of data centers and what this could mean for artificial intelligence,Patterns,2025,article,sustainable_computing,3,71.0,71.0,16.282156334231807,71,15.9504,True,True,post,thecarbonandwaterfootprintsofdatacentersandwhatthiscouldmeanforartificialintelligence,31.02498130377243,landmark,True1203156891177,10.48550/arxiv.2104.10350,2104.10350,Carbon Emissions and Large Neural Network Training,arXiv (Cornell University),2021,preprint,sustainable_computing,6,47.0,77.0,29.52943615257048,122,,True,True,baseline,carbonemissionsandlargeneuralnetworktraining,29.52943615257048,landmark,True1214407592994,10.1145/3718363,2409.12626,Green Federated Learning: A New Era of Green Aware AI,ACM Computing Surveys,2025,article,sustainable_computing,9,65.0,65.0,14.906199460916442,65,14.5832,True,True,post,greenfederatedlearninganeweraofgreenawareai,28.403151897819832,landmark,True1224401304802,10.2172/2417618,,Best Practices Guide for Energy-Efficient Data Center Design,,2024,report,sustainable_computing,3,19.0,25.0,28.26470588235294,25,,True,True,post,bestpracticesguideforenergyefficientdatacenterdesign,28.26470588235294,landmark,True1233137672894,10.1109/tgcn.2021.3067374,,SSUR: An Approach to Optimizing Virtual Machine Allocation Strategy Based on User Requirements for Cloud Data Center,IEEE Transactions on Green Communications and Networking,2021,article,sustainable_computing,5,89.0,105.0,27.80457982156345,160,29.0452,True,False,baseline,ssuranapproachtooptimizingvirtualmachineallocationstrategybasedonuserrequirementsforclouddatacenter,27.80457982156345,landmark,True1244411541921,10.1145/3715275.3732007,2501.16548,From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate,"ACM Conference on Fairness, Accountability, and Transparency (FAccT)",2025,conference-paper,sustainable_computing,2,62.0,62.0,60.833434492326376,62,144.145,True,True,post,fromefficiencygainstoreboundeffectstheproblemofjevonsparadoxinaispolarizedenvironmentaldebate,27.09223719484353,landmark,True1252803823791,10.1016/j.suscom.2018.05.002,,A review of thermal management and innovative cooling strategies for data center,Sustainable Computing Informatics and Systems,2018,article,sustainable_computing,3,24.0,50.0,26.509854547204004,324,37.3606,False,False,baseline,areviewofthermalmanagementandinnovativecoolingstrategiesfordatacenter,26.509854547204004,landmark,True1263037032032,10.1609/aaai.v34i09.7123,,Energy and Policy Considerations for Modern Deep Learning Research,Proceedings of the AAAI Conference on Artificial Intelligence,2020,conference-paper,sustainable_computing,4,31.0,98.0,25.856691345196346,755,70.4108,True,True,baseline,energyandpolicyconsiderationsformoderndeeplearningresearch,25.856691345196346,landmark,True127