Ultra-low-power Image Classification on Neuromorphic Hardware

TL;DR

This paper introduces Quartz, a novel temporal conversion method for spiking neural networks that significantly reduces energy consumption and latency while maintaining high accuracy, suitable for neuromorphic hardware like Intel's Loihi.

Contribution

Quartz is a new temporal ANN-to-SNN conversion technique based on time to first spike, offering high accuracy with minimal spikes and easy hardware implementation.

Findings

Quartz achieves high accuracy on MNIST, CIFAR10, and ImageNet.

It reduces power consumption, throughput, and latency compared to rate coding methods.

Implementation on Loihi demonstrates practical benefits in real hardware.

Abstract

Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. The number of binary activations, called spikes, is proportional to the power consumed when executed on neuromorphic hardware. Training such SNNs using backpropagation through time for vision tasks that rely mainly on spatial features is computationally costly. Training a stateless artificial neural network (ANN) to then convert the weights to an SNN is a straightforward alternative when it comes to image recognition datasets. Most conversion methods rely on rate coding in the SNN to represent ANN activation, which uses enormous amounts of spikes and, therefore, energy to encode information. Recently, temporal conversion methods have shown promising results requiring significantly fewer spikes per neuron, but sometimes complex neuron models. We propose a temporal ANN-to-SNN conversion method, which we call Quartz, that is based on the time to first spike (TTFS). Quartz achieves high classification accuracy and can be easily implemented on neuromorphic hardware while using the least amount of synaptic operations and memory accesses. It incurs a cost of two additional synapses per neuron compared to previous temporal conversion methods, which are readily available on neuromorphic hardware. We benchmark Quartz on MNIST, CIFAR10, and ImageNet in simulation to show the benefits of our method and follow up with an implementation on Loihi, a neuromorphic chip by Intel. We provide evidence that temporal coding has advantages in terms of power consumption, throughput, and latency for similar classification accuracy. Our code and models are publicly available.