A Prediction Scheme in Spiking Neural Network (SNN) Hardware for Ultra-low Power Consumption

The tremendous success of the artificial neural networks (ANNs) has led to an increase in the demand for embedded neural network hardware. In this trend, researchers aggressively studied spiking neural network (SNN) architectures due to its advantages in power consumption. Still, better SNN architectures are needed to support more neurons required in low-power systems. Therefore, we propose a new prediction scheme based on neuron potential that significantly reduces power in SNN architectures. Our prediction scheme applies to SNN architectures composed of simple Integrate and Fire (IF) neuron models without leakage. We designed an SNN hardware with our prediction scheme and verified that our scheme reduces -19.75% power consumption with only 0.85% accuracy decay.