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Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network., , , , , , and . ISCA, page 764-775. IEEE Computer Society, (2018)The impact of 3D stacking on GPU-accelerated deep neural networks: An experimental study., , , , and . 3DIC, page 1-4. IEEE, (2016)DACAPO: Accelerating Continuous Learning in Autonomous Systems for Video Analytics., , , , , , , , and . ISCA, page 1246-1261. 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)From high-level deep neural models to FPGAs., , , , , , , and . MICRO, page 17:1-17:12. IEEE Computer Society, (2016)Domain-Specific Computational Storage for Serverless Computing., , , , , , , , , and 1 other author(s). CoRR, (2023)In-Storage Domain-Specific Acceleration for Serverless Computing., , , , , , , , , and 1 other author(s). ASPLOS (2), page 530-548. ACM, (2024)Accelerated Deep Learning for the Edge-to-Cloud continuum: a Specialized Full Stack derived from Algorithms.. Georgia Institute of Technology, Atlanta, GA, USA, (2019)base-search.net (ftgeorgiatech:oai:smartech.gatech.edu:1853/61267).Mixed-Signal Charge-Domain Acceleration of Deep Neural Networks through Interleaved Bit-Partitioned Arithmetic., , , , , , , and . PACT, page 399-411. ACM, (2020)