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NeRPIM: A 4.2 mJ/frame Neural Rendering Processing-in-memory Processor with Space Encoding Block-wise Mapping for Mobile Devices.

, , , , , , and . VLSI Technology and Circuits, page 1-2. IEEE, (2023)

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NeRPIM: A 4.2 mJ/frame Neural Rendering Processing-in-memory Processor with Space Encoding Block-wise Mapping for Mobile Devices., , , , , , and . VLSI Technology and Circuits, page 1-2. IEEE, (2023)LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16., , , , , and . ISSCC, page 142-144. IEEE, (2019)HNPU: An Adaptive DNN Training Processor Utilizing Stochastic Dynamic Fixed-Point and Active Bit-Precision Searching., , , , , , and . IEEE J. Solid State Circuits, 56 (9): 2858-2869 (2021)Energy-Efficient Deep Reinforcement Learning Accelerator Designs for Mobile Autonomous Systems., , , , , and . AICAS, page 1-4. IEEE, (2021)DynaPlasia: An eDRAM In-Memory-Computing-Based Reconfigurable Spatial Accelerator with Triple-Mode Cell for Dynamic Resource Switching., , , , , , , , and . ISSCC, page 256-257. IEEE, (2023)An Energy-Efficient Deep Reinforcement Learning FPGA Accelerator for Online Fast Adaptation with Selective Mixed-precision Re-training., , , and . A-SSCC, page 1-3. IEEE, (2021)TSUNAMI: Triple Sparsity-Aware Ultra Energy-Efficient Neural Network Training Accelerator With Multi-Modal Iterative Pruning., , , , , and . IEEE Trans. Circuits Syst. I Regul. Pap., 69 (4): 1494-1506 (2022)HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices., , , , , , and . HCS, page 1-18. IEEE, (2022)7.4 GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation., , , , , , and . ISSCC, page 140-142. IEEE, (2020)A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices., , , , , and . VLSI Circuits, page 302-. IEEE, (2019)