Approximate Digital Leaky Integrate-and-fire Neurons for Energy Efficient Spiking Neural Networks

Abstract

Inherent error resilience in neuromorphic computing allows remarkable power and energy savings by exploiting approximate computing. This paper presents approximate digital leaky integrate-and-fire (LIF) neurons that leverage the approximate adders for energy efficient spiking neural networks (SNNs). Various approximate adder architectures were assessed and adopted in designing a digital LIF neuron. To demonstrate the performance of the approximate LIF neurons, a two-layer SNN with more than 1,000 digital neurons were used with the approximate adders. The results showed that the approximation errors of the lower-part OR adder (LOA) and optimized lower-part constant OR adder (OLOCA) do not affect the neuron’s spiking activities of the network significantly. In addition, they showed good power and energy efficiency. The results suggest that the LOA and OLOCA are more suitable for SNNs to achieve power- and energyefficient neuromorphic computing than the other approximate adders.