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TABLA: A unified template-based framework for accelerating statistical machine learning., , , , , , и . HPCA, стр. 14-26. IEEE Computer Society, (2016)From high-level deep neural models to FPGAs., , , , , , , и . MICRO, стр. 17:1-17:12. IEEE Computer Society, (2016)Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks., , , , , , , и . CoRR, (2017)A Computational Stack for Cross-Domain Acceleration., , , , , , , и . HPCA, стр. 54-70. IEEE, (2021)VeriGOOD-ML: An Open-Source Flow for Automated ML Hardware Synthesis., , , , , , , , , и 6 other автор(ы). ICCAD, стр. 1-7. IEEE, (2021)Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms., , , , , , , , , и 1 other автор(ы). MLCAD, стр. 119-126. ACM / IEEE, (2022)An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators., , , , , , , , , и 1 other автор(ы). CoRR, (2023)Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks., , , , , , , , , и 2 other автор(ы). MICRO, стр. 681-697. IEEE, (2020)Scale-out acceleration for machine learning., , , , , и . MICRO, стр. 367-381. ACM, (2017)In-RDBMS Hardware Acceleration of Advanced Analytics., , , , , и . Proc. VLDB Endow., 11 (11): 1317-1331 (2018)