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Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point., , , , , , , , , and 14 other author(s). NeurIPS, (2020)Improving worst-case cache performance through selective bypassing and register-indexed cache., , and . DAC, page 123:1-123:6. ACM, (2015)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)Dynamic Stashing Quantization for Efficient Transformer Training., , , and . EMNLP (Findings), page 7329-7336. Association for Computational Linguistics, (2023)Prediction-Guided Performance-Energy Trade-off with Continuous Run-Time Adaptation., , and . ISLPED, page 224-229. ACM, (2016)A Configurable Cloud-Scale DNN Processor for Real-Time AI., , , , , , , , , and 10 other author(s). ISCA, page 1-14. IEEE Computer Society, (2018)Prediction-guided performance-energy trade-off for interactive applications., , and . MICRO, page 508-520. ACM, (2015)