SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks

TL;DR

SPIDE introduces a spike-based training method for spiking neural networks that enables energy-efficient, event-driven learning with competitive accuracy on standard datasets, using purely spike-based computation during both forward and backward passes.

Contribution

This work develops a novel spike-based implicit differentiation method (SPIDE) for training SNNs, enabling fully spike-based, energy-efficient supervised learning with local weight updates.

Findings

Achieves competitive accuracy on MNIST, CIFAR-10, CIFAR-100, and CIFAR10-DVS datasets.

Demonstrates reduced energy costs through spike-based, event-driven training.

Trains SNNs with flexible structures in few time steps with sparse firing.

Abstract

Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial neural networks or direct training with surrogate gradients, require complex computation rather than spike-based operations of spiking neurons during training. In this paper, we study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method, implicit differentiation on the equilibrium state (IDE), for supervised learning with purely spike-based computation, which demonstrates the potential for energy-efficient training of SNNs. Specifically, we introduce ternary spiking neuron couples and prove that implicit differentiation can be solved by spikes based on this design, so the whole training procedure, including both forward and backward passes, is made as event-driven spike computation, and weights are updated locally with two-stage average firing rates. Then we propose to modify the reset membrane potential to reduce the approximation error of spikes. With these key components, we can train SNNs with flexible structures in a small number of time steps and with firing sparsity during training, and the theoretical estimation of energy costs demonstrates the potential for high efficiency. Meanwhile, experiments show that even with these constraints, our trained models can still achieve competitive results on MNIST, CIFAR-10, CIFAR-100, and CIFAR10-DVS. Our code is available at https://github.com/pkuxmq/SPIDE-FSNN.