Energy-Efficient High-Accuracy Spiking Neural Network Inference Using Time-Domain Neurons

TL;DR

This paper introduces a low-power, highly linear time-domain integrate-and-fire neuron circuit for spiking neural networks, achieving significant improvements in energy efficiency and accuracy on MNIST compared to traditional voltage-domain neurons.

Contribution

The paper proposes a novel time-domain neuron circuit that reduces power consumption and improves linearity, leading to higher accuracy in SNN inference.

Findings

Over 4.3x lower error rate on MNIST

Power consumption of 0.230uW per neuron

Significant energy efficiency improvement

Abstract

Due to the limitations of realizing artificial neural networks on prevalent von Neumann architectures, recent studies have presented neuromorphic systems based on spiking neural networks (SNNs) to reduce power and computational cost. However, conventional analog voltage-domain integrate-and-fire (I&F) neuron circuits, based on either current mirrors or op-amps, pose serious issues such as nonlinearity or high power consumption, thereby degrading either inference accuracy or energy efficiency of the SNN. To achieve excellent energy efficiency and high accuracy simultaneously, this paper presents a low-power highly linear time-domain I&F neuron circuit. Designed and simulated in a 28nm CMOS process, the proposed neuron leads to more than 4.3x lower error rate on the MNIST inference over the conventional current-mirror-based neurons. In addition, the power consumed by the proposed neuron circuit is simulated to be 0.230uW per neuron, which is orders of magnitude lower than the existing voltage-domain neurons.