The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks

TL;DR

This paper introduces two new spike-based classification datasets, including a novel Heidelberg digit dataset, to benchmark and compare the computational performance of spiking neural networks across software and hardware implementations.

Contribution

It provides the first standardized datasets and evaluation framework for assessing and comparing the performance of spiking neural networks.

Findings

Spike timing is crucial for classification accuracy.

The datasets enable objective performance benchmarking.

The Heidelberg digit dataset is high-fidelity and word-aligned.

Abstract

Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these networks compute, recently developed optimization techniques allow us to instantiate increasingly complex functional spiking neural networks in-silico. These methods hold the promise to build more efficient non-von-Neumann computing hardware and will offer new vistas in the quest of unraveling brain circuit function. To accelerate the development of such methods, objective ways to compare their performance are indispensable. Presently, however, there are no widely accepted means for comparing the computational performance of spiking neural networks. To address this issue, we introduce two spike-based classification datasets, broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks. To accomplish this, we developed a general audio-to-spiking conversion procedure inspired by neurophysiology. Further, we applied this conversion to an existing and a novel speech dataset. The latter is the free, high-fidelity, and word-level aligned Heidelberg digit dataset that we created specifically for this study. By training a range of conventional and spiking classifiers, we show that leveraging spike timing information within these datasets is essential for good classification accuracy. These results serve as the first reference for future performance comparisons of spiking neural networks.