Energy Efficient Training of SNN using Local Zeroth Order Method

• URL: http://arxiv.org/abs/2302.00910v2
• Date: Sun, 5 Feb 2023 11:13:47 GMT
• Title: Energy Efficient Training of SNN using Local Zeroth Order Method
• Authors: Bhaskar Mukhoty, Velibor Bojkovic, William de Vazelhes, Giulia De Masi, Huan Xiong, Bin Gu
• Abstract summary: Spiking neural networks are becoming increasingly popular for their low energy requirement in real-world tasks. SNN training algorithms face the loss of gradient information and non-differentiability due to the Heaviside function. We propose a differentiable approximation of the Heaviside in the backward pass, while the forward pass uses the Heaviside as the spiking function.
• Score: 18.81001891391638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking neural networks are becoming increasingly popular for their low energy requirement in real-world tasks with accuracy comparable to the traditional ANNs. SNN training algorithms face the loss of gradient information and non-differentiability due to the Heaviside function in minimizing the model loss over model parameters. To circumvent the problem surrogate method uses a differentiable approximation of the Heaviside in the backward pass, while the forward pass uses the Heaviside as the spiking function. We propose to use the zeroth order technique at the neuron level to resolve this dichotomy and use it within the automatic differentiation tool. As a result, we establish a theoretical connection between the proposed local zeroth-order technique and the existing surrogate methods and vice-versa. The proposed method naturally lends itself to energy-efficient training of SNNs on GPUs. Experimental results with neuromorphic datasets show that such implementation requires less than 1 percent neurons to be active in the backward pass, resulting in a 100x speed-up in the backward computation time. Our method offers better generalization compared to the state-of-the-art energy-efficient technique while maintaining similar efficiency.

Related papers

• Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation [3.5624857747396814]
  Spi neural networks (SNNs) have received widespread attention as an ultra-low energy computing paradigm. Recent studies have focused on improving the feature extraction capability of SNNs, but they suffer from inefficient and suboptimal performance. We. propose a simple yet effective temporal reversed training (TRT) method to optimize the temporal performance of SNNs.
  arXiv  Detail & Related papers  (2024-08-17T06:23:38Z)
• Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks [69.2642802272367]
  Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. Most recent methods leverage spatial and temporal backpropagation (BP), not adhering to neuromorphic properties. We propose a novel method, online pseudo-zeroth-order (OPZO) training.
  arXiv  Detail & Related papers  (2024-07-17T12:09:00Z)
• FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion [16.9748086865693]
  Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs) In this work, we introduce a lightweight Forward Temporal Bias (FTBC) technique, aimed at enhancing conversion accuracy without the computational overhead. We further propose an algorithm for finding the temporal bias only in the forward pass, thus eliminating the computational burden of backpropagation.
  arXiv  Detail & Related papers  (2024-03-27T09:25:20Z)
• Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning [24.799635365988905]
  We introduce a method to convert Physics-Informed Neural Networks (PINNs) to Spiking Neural Networks (SNNs) SNNs are expected to have higher energy efficiency compared to traditional Artificial Neural Networks (ANNs)
  arXiv  Detail & Related papers  (2023-08-31T00:21:27Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks [51.92362217307946]
  Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems. PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
  arXiv  Detail & Related papers  (2023-03-03T08:17:47Z)
• SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks [56.35403810762512]
  Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. We study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method.
  arXiv  Detail & Related papers  (2023-02-01T04:22:59Z)
• Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [70.75043144299168]
  Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. It is a challenge to efficiently train SNNs due to their non-differentiability. We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
  arXiv  Detail & Related papers  (2022-05-01T12:44:49Z)
• Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting [29.685909045226847]
  Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest because of their event-driven and energy-efficient characteristics. Current direct training approach with surrogate gradient results in SNNs with poor generalizability. We introduce the temporal efficient training (TET) approach to compensate for the loss of momentum in the gradient descent with SG.
  arXiv  Detail & Related papers  (2022-02-24T08:02:37Z)
• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [66.2457134675891]
  Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks. We propose a novel training method that does not rely on the exact reverse of the forward computation.
  arXiv  Detail & Related papers  (2021-09-29T07:46:54Z)

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.