tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks

TL;DR

tinySNN is a framework that significantly reduces memory and energy consumption of spiking neural networks while maintaining high accuracy, making SNNs more suitable for resource-constrained embedded systems.

Contribution

The paper introduces tinySNN, a novel approach that optimizes SNN models through quantization, operation reduction, and model selection to enhance efficiency without sacrificing accuracy.

Findings

Substantial reduction in memory footprint and energy use.

Maintains high accuracy comparable to baseline models.

Effective model compression for embedded applications.

Abstract

Larger Spiking Neural Network (SNN) models are typically favorable as they can offer higher accuracy. However, employing such models on the resource- and energy-constrained embedded platforms is inefficient. Towards this, we present a tinySNN framework that optimizes the memory and energy requirements of SNN processing in both the training and inference phases, while keeping the accuracy high. It is achieved by reducing the SNN operations, improving the learning quality, quantizing the SNN parameters, and selecting the appropriate SNN model. Furthermore, our tinySNN quantizes different SNN parameters (i.e., weights and neuron parameters) to maximize the compression while exploring different combinations of quantization schemes, precision levels, and rounding schemes to find the model that provides acceptable accuracy. The experimental results demonstrate that our tinySNN significantly reduces the memory footprint and the energy consumption of SNNs without accuracy loss as compared to the baseline network. Therefore, our tinySNN effectively compresses the given SNN model to achieve high accuracy in a memory- and energy-efficient manner, hence enabling the employment of SNNs for the resource- and energy-constrained embedded applications.