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Last accessed","author":"CUB.","year":"2021"},{"key":"e_1_3_2_2_6_1","volume-title":"https:\/\/developer.nvidia.com\/cudnn. Last accessed","author":"DNN.","year":"2021"},{"key":"e_1_3_2_2_7_1","volume-title":"Last accessed","year":"2021"},{"key":"e_1_3_2_2_8_1","volume-title":"https:\/\/github.com\/NVIDIA\/cutlass. Last accessed","author":"CUTLASS.","year":"2021"},{"key":"e_1_3_2_2_9_1","volume-title":"https:\/\/docs.nvidia.com\/deeplearning\/performance\/index.html. Last accessed","author":"Deep Learning Performance Guide","year":"2021"},{"key":"e_1_3_2_2_10_1","volume-title":"https:\/\/developer.nvidia.com\/drive. Last accessed","author":"Autonomous Vehicle Development Platforms","year":"2021"},{"key":"e_1_3_2_2_11_1","volume-title":"https:\/\/developer.nvidia.com\/embedded\/jetson-modules. Last accessed","author":"Jetson","year":"2021"},{"key":"e_1_3_2_2_12_1","volume-title":"Matrix Fragments for mma.m16n8k8 https:\/\/docs.nvidia.com\/cuda\/parallel-thread-execution\/index.html#warp-level-matrix-fragment-mma-1688. Last accessed","author":"Parallel Thread Execution ISA","year":"2021"},{"key":"e_1_3_2_2_13_1","volume-title":"Last accessed","author":"Tensor Cores","year":"2021"},{"key":"e_1_3_2_2_14_1","volume-title":"https:\/\/developer.nvidia.com\/tensorrt. Last accessed","author":"Tensor RT.","year":"2021"},{"key":"e_1_3_2_2_15_1","volume-title":"https:\/\/images.nvidia.com\/content\/pdf\/tesla\/184457-Tesla-P4-Datasheet-NV-Final-Letter-Web.pdf. Last accessed","author":"Tesla GPU","year":"2021"},{"key":"e_1_3_2_2_16_1","volume-title":"https:\/\/pytorch.org\/vision\/stable\/models.html. Last accessed","author":"Torchvision Models","year":"2021"},{"key":"e_1_3_2_2_17_1","volume-title":"https:\/\/on-demand.gputechconf.com\/gtc-express\/2011\/presentations\/cuda_webinars_WarpsAndOccupancy.pdf","author":"Warps","year":"2011"},{"key":"e_1_3_2_2_18_1","unstructured":"NVIDIA Tesla V100 GPU Architecture. Tech. Rep. WP-08608-001_v1.1 2017.  NVIDIA Tesla V100 GPU Architecture. Tech. Rep. WP-08608-001_v1.1 2017."},{"key":"e_1_3_2_2_19_1","first-page":"v01","volume":"09183","author":"Turing GPU","year":"2018","journal-title":"Architecture. Tech. Rep. WP-"},{"key":"e_1_3_2_2_20_1","unstructured":"Hewlett Packard Enterprise accelerates space exploration with first ever in-space commercial edge computing and artificial intelligence capabilities. https:\/\/www.hpe.com\/us\/en\/newsroom\/press-release\/2021\/02\/hewlett-packard-enterprise-accelerates-space-exploration-with-first-ever-in-space-commercial-edge-computing-and-artificial-intelligence-capabilities.html 2021. Last accessed 23 August 2021.  Hewlett Packard Enterprise accelerates space exploration with first ever in-space commercial edge computing and artificial intelligence capabilities. https:\/\/www.hpe.com\/us\/en\/newsroom\/press-release\/2021\/02\/hewlett-packard-enterprise-accelerates-space-exploration-with-first-ever-in-space-commercial-edge-computing-and-artificial-intelligence-capabilities.html 2021. Last accessed 23 August 2021."},{"key":"e_1_3_2_2_21_1","first-page":"1","volume":"1","author":"Bartlett W.","year":"2004","journal-title":"Commercial Fault Tolerance: A Tale of Two Systems. IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_3_2_2_22_1","volume-title":"The Third Conference on Systems and Machine Learning (MLSys 20)","author":"Blalock D.","year":"2020"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.jpdc.2008.12.002","volume":"69","author":"Bosilca G.","year":"2009","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"e_1_3_2_2_24_1","volume":"2014","author":"Braun C.","journal-title":"J. A-ABFT: Autonomous Algorithm-Based Fault Tolerance for Matrix Multiplications on Graphics Processing Units. In"},{"key":"e_1_3_2_2_25_1","first-page":"6","volume":"39","author":"Campbell A.","year":"1992","journal-title":"Single Event Upset Rates in Space. IEEE Transactions on Nuclear Science"},{"key":"e_1_3_2_2_26_1","volume-title":"Online Algorithm-Based Fault Tolerance for Cholesky Decomposition on Heterogeneous Systems with GPUs. In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS 16)","author":"Chen J.","year":"2016"},{"key":"e_1_3_2_2_27_1","volume-title":"13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Chen T.","year":"2018"},{"key":"e_1_3_2_2_28_1","volume-title":"Z. Online-ABFT: An Online Algorithm Based Fault Tolerance Scheme for Soft Error Detection in Iterative Methods. In ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP 13)","author":"Chen","year":"2013"},{"key":"e_1_3_2_2_29_1","volume-title":"A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction. arXiv preprint arXiv:2003.13874v4","author":"Chen Z.","year":"2021"},{"key":"e_1_3_2_2_30_1","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 19)","author":"Chen Z.","year":"2019"},{"key":"e_1_3_2_2_31_1","volume-title":"N. TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications. In 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE 20)","author":"Chen Z.","year":"2020"},{"key":"e_1_3_2_2_32_1","first-page":"2","volume":"38","author":"Chung E.","year":"2018","journal-title":"Datacenter Scale with Project Brainwave. IEEE Micro"},{"key":"e_1_3_2_2_33_1","volume-title":"Proceedings of the ACM International Conference on Supercomputing (ICS 19)","author":"Dakkak A.","year":"2019"},{"key":"e_1_3_2_2_34_1","volume-title":"A White Paper on the Benefits of Chipkill-Correct ECC for PC Server Main Memory. IBM Microelectronics division 11","author":"Dell T. J.","year":"1997"},{"key":"e_1_3_2_2_35_1","volume-title":"Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 20)","author":"Denby B.","year":"2020"},{"key":"e_1_3_2_2_36_1","volume-title":"Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units (GPGPU 09)","author":"Dimitrov M.","year":"2009"},{"key":"e_1_3_2_2_37_1","volume-title":"Silent Data Corruptions at Scale. arXiv preprint arXiv:2102.11245","author":"Dixit H. D.","year":"2021"},{"key":"e_1_3_2_2_38_1","volume-title":"Reliability Evaluation of Mixed-Precision Architectures. In 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA 19)","author":"dos Santos F. F.","year":"2019"},{"key":"e_1_3_2_2_39_1","volume-title":"CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors. arXiv preprint arXiv:1903.01042","author":"Dutta S.","year":"2019"},{"key":"e_1_3_2_2_40_1","volume-title":"Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP 21)","author":"Feng B.","year":"2021"},{"key":"e_1_3_2_2_41_1","first-page":"3","volume":"53","author":"Geist","year":"2016","journal-title":"Supercomputing's Monster in the Closet. IEEE Spectrum"},{"key":"e_1_3_2_2_42_1","volume-title":"International Conference for High Performance Computing, Networking, Storage and Analysis (SC 18)","author":"Haidar A.","year":"2018"},{"key":"e_1_3_2_2_43_1","volume-title":"Making Convolutions Resilient via Algorithm-Based Error Detection Techniques","author":"Hari S. K. S.","year":"2021"},{"key":"e_1_3_2_2_44_1","volume-title":"Proceedings of the 18th Workshop on Hot Topics in Operating System (HotOS 21)","author":"Hochschild P. H.","year":"2021"},{"key":"e_1_3_2_2_45_1","volume-title":"Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Hsieh K.","year":"2018"},{"key":"e_1_3_2_2_46_1","first-page":"6","volume":"100","author":"Huang K.-H.","year":"1984","journal-title":"Algorithm-Based Fault Tolerance for Matrix Operations. IEEE Transactions on Computers"},{"key":"e_1_3_2_2_47_1","volume-title":"Warped-DMR: Light-Weight Error Detection for GPGPU. In 2012 45th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO 12)","author":"Jeon H.","year":"2012"},{"key":"e_1_3_2_2_48_1","volume-title":"Dissecting the NVidia Turing T4 GPU via Microbenchmarking. arXiv preprint arXiv:1903.07486","author":"Jia Z.","year":"2019"},{"key":"e_1_3_2_2_49_1","first-page":"4","volume":"13","author":"Kang D.","year":"2020","journal-title":"BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. Proceedings of the VLDB Endowment"},{"key":"e_1_3_2_2_50_1","first-page":"11","volume":"10","author":"Kang D.","year":"2017","journal-title":"Scale. Proceedings of the VLDB Endowment"},{"key":"e_1_3_2_2_51_1","volume-title":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 20)","author":"Kim H.","year":"2020"},{"key":"e_1_3_2_2_52_1","volume-title":"Proceedings of the 52nd Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO 19)","author":"Koppula S.","year":"2019"},{"key":"e_1_3_2_2_53_1","volume-title":"Advances in Neural Information Processing Systems (NIPS 12)","author":"Krizhevsky A.","year":"2012"},{"key":"e_1_3_2_2_54_1","unstructured":"LaBel K. A. NASA and COTS Electronics: Past Approach and Successes-Future Considerations.  LaBel K. A. NASA and COTS Electronics: Past Approach and Successes-Future Considerations."},{"key":"e_1_3_2_2_55_1","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 17)","author":"Li G.","year":"2017"},{"key":"e_1_3_2_2_56_1","volume-title":"Modeling Soft-Error Propagation in Programs. In 2018 48th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN 18)","author":"Li G.","year":"2018"},{"key":"e_1_3_2_2_57_1","volume-title":"Efficient Soft-Error Detection for Low-Precision Deep Learning Recommendation Models. arXiv preprint arXiv:2103.00130","author":"Li S.","year":"2021"},{"key":"e_1_3_2_2_58_1","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 19)","author":"Li S.","year":"2019"},{"key":"e_1_3_2_2_59_1","volume-title":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS 20)","author":"Li Y.","year":"2020"},{"key":"e_1_3_2_2_60_1","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 16)","author":"Liu Q.","year":"2016"},{"key":"e_1_3_2_2_61_1","volume-title":"PyTorchFI: A Runtime Perturbation Tool for DNNs. In 2020 50th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W 20)","author":"Mahmoud A.","year":"2020"},{"key":"e_1_3_2_2_62_1","volume-title":"HarDNN: Feature Map Vulnerability Evaluation in CNNs. arXiv preprint arXiv:2002.09786","author":"Mahmoud A.","year":"2020"},{"key":"e_1_3_2_2_63_1","volume-title":"Optimizing Software-Directed Instruction Replication for GPU Error Detection. In International Conference for High Performance Computing, Networking, Storage and Analysis (SC 18)","author":"Mahmoud A.","year":"2018"},{"key":"e_1_3_2_2_64_1","volume-title":"A Survey of Techniques for Modeling and Improving Reliability of Computing Systems","author":"Mittal S.","year":"2015"},{"key":"e_1_3_2_2_65_1","first-page":"3573","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV 19)","author":"Mullapudi R. T.","year":"2019"},{"key":"e_1_3_2_2_66_1","volume-title":"Deep Learning Recommendation Model for Personalization and Recommendation Systems. arXiv preprint arXiv:1906.00091","author":"Naumov M.","year":"2019"},{"key":"e_1_3_2_2_67_1","volume-title":"Sanity-Check: Boosting the Reliability of Safety-Critical Deep Neural Network Applications. In 2019 IEEE 28th Asian Test Symposium (ATS 19)","author":"Ozen E.","year":"2019"},{"key":"e_1_3_2_2_68_1","volume-title":"Concurrent Monitoring of Operational Health in Neural Networks Through Balanced Output Partitions. In 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC 20)","author":"Ozen E.","year":"2020"},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"crossref","DOI":"10.1145\/3400302.3415680","volume-title":"Just Say Zero: Containing Critical Bit-Error Propagation in Deep Neural Networks with Anomalous Feature Suppression. In 2020 IEEE\/ACM International Conference On Computer Aided Design (ICCAD 20)","author":"Ozen E.","year":"2020"},{"key":"e_1_3_2_2_70_1","volume-title":"Proceedings of the International Symposium on Code Generation and Optimization (CGO 05)","author":"Reis G. A.","year":"2005"},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"crossref","unstructured":"Russakovsky O. Deng J. Su H. Krause J. Satheesh S. Ma S. Huang Z. Karpathy A. Khosla A. Bernstein M. Berg A. C. and Fei-Fei L. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115 3 (2015) 211--252.  Russakovsky O. Deng J. Su H. Krause J. Satheesh S. Ma S. Huang Z. Karpathy A. Khosla A. Bernstein M. Berg A. C. and Fei-Fei L. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115 3 (2015) 211--252.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_2_72_1","volume-title":"2018 IEEE International Reliability Physics Symposium (IRPS) Keynote","author":"Saxena N.","year":"2018"},{"key":"e_1_3_2_2_73_1","volume-title":"FACER: A Universal Framework for Detecting Anomalous Operation of Deep Neural Networks. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC 21)","author":"Schorn C.","year":"2020"},{"key":"e_1_3_2_2_74_1","volume-title":"Efficient On-Line Error Detection and Mitigation for Deep Neural Network Accelerators. In International Conference on Computer Safety, Reliability, and Security (SAFECOMP 18)","author":"Schorn C.","year":"2018"},{"key":"e_1_3_2_2_75_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 17)","author":"Shen H.","year":"2017"},{"key":"e_1_3_2_2_77_1","volume-title":"Swapcodes: Error Codes for Hardware-Software Cooperative GPU Pipeline Error Detection. In 2018 51st Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO 18)","author":"Sullivan M. B.","year":"2018"},{"key":"e_1_3_2_2_78_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML 19)","author":"Tan M.","year":"2019"},{"key":"e_1_3_2_2_79_1","first-page":"17322","volume":"5","author":"Torres-Huitzil C.","year":"2017","journal-title":"Error Tolerance in Neural Networks: A Review. IEEE Access"},{"key":"e_1_3_2_2_80_1","volume-title":"Real-World Design and Evaluation of Compiler-Managed GPU Redundant Multithreading. In 2014 ACM\/IEEE 41st Annual International Symposium on Computer Architecture (ISCA 14)","author":"Wadden J.","year":"2014"},{"key":"e_1_3_2_2_81_1","volume-title":"Edge, Cloud to Earth Orbit in Preparation for Mars Missions. https:\/\/www.hpcwire.com\/2021\/02\/12\/microsoft-hpe-bringing-ai-edge-cloud-to-earth-orbit-in-preparation-for-mars-missions\/. Last accessed","author":"Weiss T. R.","year":"2021"},{"key":"e_1_3_2_2_82_1","first-page":"4","volume":"52","author":"Williams S.","year":"2009","journal-title":"Insightful Visual Performance Model for Multicore Architectures. Communications of the ACM"},{"key":"e_1_3_2_2_83_1","volume-title":"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 16)","author":"Wu P.","year":"2016"},{"key":"e_1_3_2_2_84_1","volume-title":"2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE 21)","author":"Yang L.","year":"2021"},{"key":"e_1_3_2_2_85_1","volume-title":"Proceedings of the ACM International Conference on Supercomputing (ICS 19)","author":"Zamani H.","year":"2019"},{"key":"e_1_3_2_2_86_1","volume-title":"FT-BLAS: A High Performance BLAS Implementation With Online Fault Tolerance. arXiv preprint arXiv:2104.00897","author":"Zhai Y.","year":"2021"},{"key":"e_1_3_2_2_87_1","volume-title":"Proceedings of the 55th Annual Design Automation Conference (DAC 18)","author":"Zhang J.","year":"2018"},{"key":"e_1_3_2_2_88_1","volume-title":"2018 USENIX Annual Technical Conference (USENIX ATC 18)","author":"Zhang M.","year":"2018"},{"key":"e_1_3_2_2_89_1","first-page":"07","volume":"32","author":"Zhao K.","year":"2021","journal-title":"Z. 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