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<p>There is a growing necessity for edge training to adapt to dynamically changing environments. Neuromorphic computing represents a significant pathway for highly efficient intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural networks based on backpropagation. We developed a multi-core neuromorphic architecture with Feedforward-Propagation, Back-Propagation, and Weight-Gradient engines in each core, supporting highly efficient parallel computing at both the engine and core levels, achieving 190% ~ 330% performance of Jetson Orin. It combines various data flows and sparse computation optimization by fully leveraging the sparsity in spiking neural network training, obtaining a high energy efficiency of 1.05TFLOPS/W@ FP16 @ 28 nm, 55 ~ 85% reduction of memory access compared to A100 GPU in the training. Additionally, we deployed the architecture on Field Programmable Gate Arrays, successfully demonstrating 20-core deep spiking network training and 5-worker federated learning. Our study develops the first multi-core neuromorphic architecture supporting direct training of spiking neural network, facilitating neuromorphic computing in edge-learnable applications.</p>

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A highly energy-efficient multi-core neuromorphic architecture for training deep spiking neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Mingjing Li,
  • Huihui Zhou,
  • Xiaofeng Xu,
  • Zhiwei Zhong,
  • Puli Quan,
  • Xueke Zhu,
  • Yanyu Lin,
  • Wenjie Lin,
  • Xiaosha Li,
  • Dong Wang,
  • Junchao Zhang,
  • Yunhao Ma,
  • Xiaole Cui,
  • Wei Wang,
  • Qingyan Meng,
  • Zhengyu Ma,
  • Guoqi Li,
  • Xiaoxin Cui,
  • Yonghong Tian

摘要

There is a growing necessity for edge training to adapt to dynamically changing environments. Neuromorphic computing represents a significant pathway for highly efficient intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural networks based on backpropagation. We developed a multi-core neuromorphic architecture with Feedforward-Propagation, Back-Propagation, and Weight-Gradient engines in each core, supporting highly efficient parallel computing at both the engine and core levels, achieving 190% ~ 330% performance of Jetson Orin. It combines various data flows and sparse computation optimization by fully leveraging the sparsity in spiking neural network training, obtaining a high energy efficiency of 1.05TFLOPS/W@ FP16 @ 28 nm, 55 ~ 85% reduction of memory access compared to A100 GPU in the training. Additionally, we deployed the architecture on Field Programmable Gate Arrays, successfully demonstrating 20-core deep spiking network training and 5-worker federated learning. Our study develops the first multi-core neuromorphic architecture supporting direct training of spiking neural network, facilitating neuromorphic computing in edge-learnable applications.