D-SELD: Dataset-Scalable Exemplar LCA-Decoder

TL;DR

This paper introduces a scalable encoder-decoder method for training Spiking Neural Networks that significantly improves accuracy on large datasets like ImageNet and CIFAR100, addressing computational and memory challenges.

Contribution

The paper presents a novel Dataset-Scalable Exemplar LCA-Decoder that enhances SNN training efficiency and accuracy on large datasets, surpassing previous benchmarks.

Findings

Achieved 80.75% top-1 accuracy on ImageNet.

Achieved 79.32% top-1 accuracy on CIFAR100.

Reduced computational and memory demands for SNN training.

Abstract

Neuromorphic computing has recently gained significant attention as a promising approach for developing energy-efficient, massively parallel computing systems inspired by the spiking behavior of the human brain and natively mapping Spiking Neural Networks (SNNs). Effective training algorithms for SNNs are imperative for increased adoption of neuromorphic platforms; however, SNN training continues to lag behind advances in other classes of ANN. In this paper, we reduce this gap by proposing an innovative encoder-decoder technique that leverages sparse coding and the Locally Competitive Algorithm (LCA) to provide an algorithm specifically designed for neuromorphic platforms. Using our proposed Dataset-Scalable Exemplar LCA-Decoder we reduce the computational demands and memory requirements associated with training SNNs using error backpropagation methods on increasingly larger training sets. We offer a solution that can be scalably applied to datasets of any size. Our results show the highest reported top-1 test accuracy using SNNs on the ImageNet and CIFAR100 datasets, surpassing previous benchmarks. Specifically, we achieved a record top-1 accuracy of 80.75% on ImageNet (ILSVRC2012 validation set) and 79.32% on CIFAR100 using SNNs.