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Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms.

, , , , , , , , , , and . MLCAD, page 119-126. ACM / IEEE, (2022)

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Machine Learning Acceleration., and . IEEE Micro, 39 (5): 6-7 (2019)In-DRAM near-data approximate acceleration for GPUs., , , , , and . PACT, page 34:1-34:14. ACM, (2018)AxGames: Towards Crowdsourcing Quality Target Determination in Approximate Computing., , , , and . ASPLOS, page 623-636. ACM, (2016)Test Instruction Set (TIS) for High Level Self-Testing of CPU Cores., , and . Asian Test Symposium, page 158-163. IEEE Computer Society, (2004)Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation., , and . CoRR, (2019)Conscious AI., and . CoRR, (2021)In-Storage Domain-Specific Acceleration for Serverless Computing., , , , , , , , , and 1 other author(s). ASPLOS (2), page 530-548. ACM, (2024)Tandem Processor: Grappling with Emerging Operators in Neural Networks., , , , , , , , , and 2 other author(s). ASPLOS (2), page 1165-1182. ACM, (2024)VeriGOOD-ML: An Open-Source Flow for Automated ML Hardware Synthesis., , , , , , , , , and 6 other author(s). ICCAD, page 1-7. IEEE, (2021)A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks., , , , , , , , , and . MICRO, page 175-188. IEEE Computer Society, (2018)