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Improving worst-case cache performance through selective bypassing and register-indexed cache.

, , and . DAC, page 123:1-123:6. ACM, (2015)

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Improving worst-case cache performance through selective bypassing and register-indexed cache., , and . DAC, page 123:1-123:6. ACM, (2015)Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point., , , , , , , , , and 14 other author(s). NeurIPS, (2020)Slack-aware opportunistic monitoring for real-time systems., , , and . RTAS, page 203-214. IEEE Computer Society, (2014)Run-time monitoring with adjustable overhead using dataflow-guided filtering., , , and . HPCA, page 662-674. IEEE Computer Society, (2015)Worst-case execution time analysis for parallel run-time monitoring., and . DAC, page 421-429. ACM, (2012)FlexCache: Field Extensible Cache Controller Architecture Using On-chip Reconfigurable Fabric., , and . FPL, page 244-251. IEEE Computer Society, (2011)Prediction-Guided Performance-Energy Trade-off with Continuous Run-Time Adaptation., , and . ISLPED, page 224-229. ACM, (2016)Dynamic Stashing Quantization for Efficient Transformer Training., , , and . EMNLP (Findings), page 7329-7336. Association for Computational Linguistics, (2023)Inside Project Brainwave's Cloud-Scale, Real-Time AI Processor., , , , , , , , , and 10 other author(s). IEEE Micro, 39 (3): 20-28 (2019)Flexible and Efficient Instruction-Grained Run-Time Monitoring Using On-Chip Reconfigurable Fabric., , , , and . MICRO, page 137-148. IEEE Computer Society, (2010)