FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks

TL;DR

This paper introduces a gradient approximation-free training framework for Spiking Neural Networks using a Forward-Forward approach, improving accuracy and efficiency over existing methods, especially for edge device deployment.

Contribution

It proposes a novel FF-based training method that treats spiking activations as black-box modules, reducing computational complexity and eliminating the need for gradient approximation.

Findings

Achieves high test accuracies on MNIST, Fashion-MNIST, and CIFAR-10 datasets.

Reduces memory access and computational power consumption.

Surpasses all existing FF-based SNN approaches.

Abstract

Spiking Neural Networks (SNNs) offer a biologically plausible framework for energy-efficient neuromorphic computing. However, it is a challenge to train SNNs due to their non-differentiability, efficiently. Existing gradient approximation approaches frequently sacrifice accuracy and face deployment limitations on edge devices due to the substantial computational requirements of backpropagation. To address these challenges, we propose a Forward-Forward (FF) based gradient approximation-free training framework for Spiking Neural Networks, which treats spiking activations as black-box modules, thereby eliminating the need for gradient approximation while significantly reducing computational complexity. Furthermore, we introduce a class-aware complexity adaptation mechanism that dynamically optimizes the loss function based on inter-class difficulty metrics, enabling efficient allocation of network resources across different categories. Experimental results demonstrate that our proposed training framework achieves test accuracies of 99.58%, 92.13%, and 75.64% on the MNIST, Fashion-MNIST, and CIFAR-10 datasets, respectively, surpassing all existing FF-based SNN approaches. Additionally, our proposed method exhibits significant advantages in terms of memory access and computational power consumption.