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Large-scale neural networks implemented with Non-Volatile Memory as the synaptic weight element: Impact of conductance response.

, , , , , , , , , and . ESSDERC, page 440-443. IEEE, (2016)

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Large-scale neural networks implemented with Non-Volatile Memory as the synaptic weight element: Impact of conductance response., , , , , , , , , and . ESSDERC, page 440-443. IEEE, (2016)A Multi-Mode 8K-MAC HW-Utilization-Aware Neural Processing Unit with a Unified Multi-Precision Datapath in 4nm Flagship Mobile SoC., , , , , , , , , and 7 other author(s). ISSCC, page 246-248. IEEE, (2022)Accelerating machine learning with Non-Volatile Memory: Exploring device and circuit tradeoffs., , , , , , , , and . ICRC, page 1-8. IEEE Computer Society, (2016)An 11.5TOPS/W 1024-MAC Butterfly Structure Dual-Core Sparsity-Aware Neural Processing Unit in 8nm Flagship Mobile SoC., , , , , , , and . ISSCC, page 130-132. IEEE, (2019)ReRAM-based synaptic device for neuromorphic computing., , , and . ISCAS, page 1054-1057. IEEE, (2014)Sparsity-Aware and Re-configurable NPU Architecture for Samsung Flagship Mobile SoC., , , , , , , , , and 13 other author(s). ISCA, page 15-28. IEEE, (2021)Reducing circuit design complexity for neuromorphic machine learning systems based on Non-Volatile Memory arrays., , , , , , , , and . ISCAS, page 1-4. IEEE, (2017)9.5 A 6K-MAC Feature-Map-Sparsity-Aware Neural Processing Unit in 5nm Flagship Mobile SoC., , , , , , , , , and 2 other author(s). ISSCC, page 152-154. IEEE, (2021)