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Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits.

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NeuroSim: A Circuit-Level Macro Model for Benchmarking Neuro-Inspired Architectures in Online Learning., , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 37 (12): 3067-3080 (2018)Two-Way Transpose Multibit 6T SRAM Computing-in-Memory Macro for Inference-Training AI Edge Chips., , , , , , , , , and 15 other author(s). IEEE J. Solid State Circuits, 57 (2): 609-624 (2022)ENNA: An Efficient Neural Network Accelerator Design Based on ADC-Free Compute-In-Memory Subarrays., , , and . IEEE Trans. Circuits Syst. I Regul. Pap., 70 (1): 353-363 (January 2023)Achieving High In Situ Training Accuracy and Energy Efficiency with Analog Non-Volatile Synaptic Devices., , , , and . ACM Trans. Design Autom. Electr. Syst., 27 (4): 37:1-37:19 (2022)Hardware-aware Quantization/Mapping Strategies for Compute-in-Memory Accelerators., , and . ACM Trans. Design Autom. Electr. Syst., 28 (3): 34:1-34:23 (2023)Energy-Efficient Adaptive Computing With Multifunctional Memory., , , , , and . IEEE Trans. Circuits Syst. II Express Briefs, 64-II (2): 191-195 (2017)Technological exploration of RRAM crossbar array for matrix-vector multiplication., , , , , , and . ASP-DAC, page 106-111. IEEE, (2015)Benchmark Non-volatile and Volatile Memory Based Hybrid Precision Synapses for In-situ Deep Neural Network Training., and . ASP-DAC, page 422-427. IEEE, (2020)Overcoming Challenges for Achieving High in-situ Training Accuracy with Emerging Memories., , , , and . DATE, page 1025-1030. IEEE, (2020)MNSIM: Simulation platform for memristor-based neuromorphic computing system., , , , , , , , , and 2 other author(s). DATE, page 469-474. IEEE, (2016)