A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training

• URL: http://arxiv.org/abs/2412.05302v3
• Date: Mon, 30 Dec 2024 01:29:09 GMT
• Title: A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training
• Authors: Mingjing Li, Huihui Zhou, Xiaofeng Xu, Zhiwei Zhong, Puli Quan, Xueke Zhu, Yanyu Lin, Wenjie Lin, Hongyu Guo, Junchao Zhang, Yunhao Ma, Wei Wang, Qingyan Meng, Zhengyu Ma, Guoqi Li, Xiaoxin Cui, Yonghong Tian,
• Abstract summary: We develop a multi-core neuromorphic architecture supporting the direct SNN training. We obtain a high energy efficiency of 1.05TFLOPS/W@ FP16 @ 28nm, 55 85% reduction of DRAM access compared to A100 GPU in SNN trainings.
• Score: 40.2426933591366
• License:
• Abstract: There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural networks (SNNs) based on backpropagation. We develop a multi-core neuromorphic architecture with Feedforward-Propagation, Back-Propagation, and Weight-Gradient engines in each core, supporting high efficient parallel computing at both the engine and core levels. It combines various data flows and sparse computation optimization by fully leveraging the sparsity in SNN training, obtaining a high energy efficiency of 1.05TFLOPS/W@ FP16 @ 28nm, 55 ~ 85% reduction of DRAM access compared to A100 GPU in SNN trainings, and a 20-core deep SNN training and a 5-worker federated learning on FPGAs. Our study develops the first multi-core neuromorphic architecture supporting the direct SNN training, facilitating the neuromorphic computing in edge-learnable applications.

Related papers

• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods [33.377770671553336]
  Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs) In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance.
  arXiv  Detail & Related papers  (2024-05-06T09:58:54Z)
• SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence [51.6943465041708]
  Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips.
  arXiv  Detail & Related papers  (2023-10-25T13:15:17Z)
• Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks [7.271032282434803]
  We propose a Multi-Scale Evolutionary Neural Architecture Search (MSE-NAS) for Spiking Neural Networks (SNNs) MSE-NAS evolves individual neuron operation, self-organized integration of multiple circuit motifs, and global connectivity across motifs through a brain-inspired indirect evaluation function, Representational Dissimilarity Matrices (RDMs) The proposed algorithm achieves state-of-the-art (SOTA) performance with shorter simulation steps on static datasets and neuromorphic datasets.
  arXiv  Detail & Related papers  (2023-04-21T05:36:37Z)
• PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks [1.6172800007896282]
  We propose a novel learning algorithm inspired by predictive coding theory. We show that it can perform supervised learning fully autonomously and successfully as the backprop. This method achieves a favorable performance compared to the state-of-the-art multi-layer SNNs.
  arXiv  Detail & Related papers  (2022-11-24T09:56:02Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware [0.11744028458220425]
  Several edge deep learning hardware accelerators have been released that specifically focus on reducing the power and area consumed by deep neural networks (DNNs) Spiked neural networks (SNNs) which operate on discrete time-series data have been shown to achieve substantial power reductions when deployed on specialized neuromorphic event-based/asynchronous hardware. In this work, we provide a general guide to converting pre-trained DNNs into SNNs while also presenting techniques to improve the deployment of converted SNNs on neuromorphic hardware.
  arXiv  Detail & Related papers  (2022-10-10T20:27:19Z)
• Training Spiking Neural Networks with Local Tandem Learning [96.32026780517097]
  Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient than their predecessors. In this paper, we put forward a generalized learning rule, termed Local Tandem Learning (LTL) We demonstrate rapid network convergence within five training epochs on the CIFAR-10 dataset while having low computational complexity.
  arXiv  Detail & Related papers  (2022-10-10T10:05:00Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.