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SNPU: An Energy-Efficient Spike Domain Deep-Neural-Network Processor With Two-Step Spike Encoding and Shift-and-Accumulation Unit., , , , , и . IEEE J. Solid State Circuits, 58 (10): 2812-2825 (октября 2023)ECIM: Exponent Computing in Memory for an Energy-Efficient Heterogeneous Floating-Point DNN Training Processor., , , , , и . IEEE Micro, 42 (1): 99-107 (2022)NeRPIM: A 4.2 mJ/frame Neural Rendering Processing-in-memory Processor with Space Encoding Block-wise Mapping for Mobile Devices., , , , , , и . VLSI Technology and Circuits, стр. 1-2. IEEE, (2023)PNNPU: A Fast and Efficient 3D Point Cloud-based Neural Network Processor with Block-based Point Processing for Regular DRAM Access., , , и . HCS, стр. 1-23. IEEE, (2021)An Energy-efficient Floating-Point DNN Processor using Heterogeneous Computing Architecture with Exponent-Computing-in-Memory., , , , , , , и . HCS, стр. 1-20. IEEE, (2021)An Energy-Efficient Deep Neural Network Training Processor with Bit-Slice-Level Reconfigurability and Sparsity Exploitation., , , , , , и . COOL CHIPS, стр. 1-3. IEEE, (2021)A 13.7 TFLOPS/W Floating-point DNN Processor using Heterogeneous Computing Architecture with Exponent-Computing-in-Memory., , , , , , и . VLSI Circuits, стр. 1-2. IEEE, (2021)OmniDRL: A 29.3 TFLOPS/W Deep Reinforcement Learning Processor with Dualmode Weight Compression and On-chip Sparse Weight Transposer., , , , , , и . VLSI Circuits, стр. 1-2. IEEE, (2021)7.4 GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation., , , , , , и . ISSCC, стр. 140-142. IEEE, (2020)A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices., , , , , и . VLSI Circuits, стр. 302-. IEEE, (2019)