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Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration.

, , , , and . ISCA, page 66-77. IEEE Computer Society, (2016)

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DACAPO: Accelerating Continuous Learning in Autonomous Systems for Video Analytics., , , , , , , , and . ISCA, page 1246-1261. IEEE, (2024)Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network., , , , , , and . ISCA, page 764-775. IEEE Computer Society, (2018)Accelerating String-key Learned Index Structures via Memoization-based Incremental Training., , , , , and . Proc. VLDB Endow., 17 (8): 1802-1815 (April 2024)LVS: A Learned Video Storage for Fast and Efficient Video Understanding., and . CVPR Workshops, page 8085-8093. IEEE, (2024)TABLA: A unified template-based framework for accelerating statistical machine learning., , , , , , and . HPCA, page 14-26. IEEE Computer Society, (2016)Neural acceleration for GPU throughput processors., , , , and . MICRO, page 482-493. ACM, (2015)Locality-aware dynamic VM reconfiguration on MapReduce clouds., , , , and . HPDC, page 27-36. ACM, (2012)Multi-model Machine Learning Inference Serving with GPU Spatial Partitioning., , , , , and . CoRR, (2021)NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing., , , , , , , , and . ASPLOS (3), page 722-737. ACM, (2024)From high-level deep neural models to FPGAs., , , , , , , and . MICRO, page 17:1-17:12. IEEE Computer Society, (2016)