E-NPU: A 34~126nj/class Event-Driven Adaptive Neural SoC with Signal-Dynamics-Aware Feature Clustering and Multi-Model In-Memory Inference/Training for Personalized Medical Wearables

Fengshi Tian, Jinbo Chen, Kunming Shao, Zilu Liu, Jiakun Zheng, Hui Wu, Chaoming Fang,Xiaomeng Wang, Ziyang Shen, Pingcheng Dong, Yuan Yao, Xuliang Wang,Jie Yang,Mohamad Sawan, Chi-Ying Tsui, Kwang-Ting Cheng

2025 IEEE Custom Integrated Circuits Conference (CICC)（2025）

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Key words

Feature Clustering,Neural Network,Signal Processing,Convolutional Neural Network,Energy Efficiency,Layer-by-layer,Real-world Scenarios,Localization Error,Compressor,Random Matrix,Spiking Neural Networks,Direct Feedback,Spike Trains,Motor Imagery,Local Training,Least Significant Bit,Most Significant Bit,Deployment Model,Binary Network,Remote Memory

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