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High performance reconfigurable computing for numerical simulation and deep learning., , , , , и . CCF Trans. High Perform. Comput., 2 (2): 196-208 (2020)A Fully-Pipelined FPGA Design for Tree-Reweighted Message Passing Algorithm., , и . FCCM, стр. 177. IEEE Computer Society, (2014)F-CNN: An FPGA-based framework for training Convolutional Neural Networks., , , , , , , и . ASAP, стр. 107-114. IEEE Computer Society, (2016)Optimizing Convolutional Neural Networks on the Sunway TaihuLight Supercomputer., , , , , и . ACM Trans. Archit. Code Optim., 15 (1): 13:1-13:26 (2018)Parallelizing cryo-EM 3D reconstruction on GPU cluster with a partitioned and streamed model., , , , , и . ICS, стр. 13-23. ACM, (2019)Relation-oriented resource allocation for multi-accelerator systems., , , , , , , , и . ASAP, стр. 243-244. IEEE Computer Society, (2016)Generalized GPU Acceleration for Applications Employing Finite-Volume Methods., , , , , , , , , и . CCGrid, стр. 126-135. IEEE Computer Society, (2016)Large-Scale Automatic K-Means Clustering for Heterogeneous Many-Core Supercomputer., , , , , , , , и . IEEE Trans. Parallel Distributed Syst., 31 (5): 997-1008 (2020)swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight., , , , , , , и . CoRR, (2019)Scaling the Training of Recurrent Neural Networks on Sunway TaihuLight Supercomputer., , , , , , , , , и . ICCS (1), том 11536 из Lecture Notes in Computer Science, стр. 427-440. Springer, (2019)