Navigating Local Minima in Quantized Spiking Neural Networks

TL;DR

This paper investigates how periodically boosting the learning rate can help Quantized Spiking Neural Networks overcome local minima, leading to improved training performance on complex datasets.

Contribution

It introduces a systematic evaluation of cosine-annealed learning rate schedules with adaptive optimization for QSNNs, demonstrating near state-of-the-art results.

Findings

Boosted learning rate improves navigation of loss landscapes.

Achieves near state-of-the-art performance on complex datasets.

Systematic empirical evaluation of training techniques for QSNNs.

Abstract

Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms. However, these networks face challenges when trained using error backpropagation, due to the absence of gradient signals when applying hard thresholds. The broadly accepted trick to overcoming this is through the use of biased gradient estimators: surrogate gradients which approximate thresholding in Spiking Neural Networks (SNNs), and Straight-Through Estimators (STEs), which completely bypass thresholding in Quantized Neural Networks (QNNs). While noisy gradient feedback has enabled reasonable performance on simple supervised learning tasks, it is thought that such noise increases the difficulty of finding optima in loss landscapes, especially during the later stages of optimization. By periodically boosting the Learning Rate (LR) during training, we expect the network can navigate unexplored solution spaces that would otherwise be difficult to reach due to local minima, barriers, or flat surfaces. This paper presents a systematic evaluation of a cosine-annealed LR schedule coupled with weight-independent adaptive moment estimation as applied to Quantized SNNs (QSNNs). We provide a rigorous empirical evaluation of this technique on high precision and 4-bit quantized SNNs across three datasets, demonstrating (close to) state-of-the-art performance on the more complex datasets. Our source code is available at this link: https://github.com/jeshraghian/QSNNs.