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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training, , , , и . (2017)cite arxiv:1712.01887Comment: we find 99.9% of the gradient exchange in distributed SGD is redundant; we reduce the communication bandwidth by two orders of magnitude without losing accuracy.Hardware-Centric AutoML for Mixed-Precision Quantization., , , , и . Int. J. Comput. Vis., 128 (8): 2035-2048 (2020)A Configurable Multi-Precision CNN Computing Framework Based on Single Bit RRAM., , , , , , , и . DAC, стр. 56. ACM, (2019)Long live TIME: improving lifetime for training-in-memory engines by structured gradient sparsification., , , , , , и . DAC, стр. 107:1-107:6. ACM, (2018)TorchSparse: Efficient Point Cloud Inference Engine., , , , и . MLSys, mlsys.org, (2022)Tricriteria Optimization-Coordination Motion of Dual-Redundant-Robot Manipulators for Complex Path Planning., , , , , и . IEEE Trans. Control. Syst. Technol., 26 (4): 1345-1357 (2018)APQ: Joint Search for Network Architecture, Pruning and Quantization Policy., , , , , , , и . CVPR, стр. 2075-2084. Computer Vision Foundation / IEEE, (2020)MCUNet: Tiny Deep Learning on IoT Devices., , , , , и . NeurIPS, (2020)Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training., , , , и . ICLR (Poster), OpenReview.net, (2018)HAQ: Hardware-Aware Automated Quantization., , , , и . CoRR, (2018)