Fully Memristive SNNs with Temporal Coding for Fast and Low-power Edge Computing

Abstract

SNNs with temporal coding (TC), inspired by the human visual system, have a powerful ability to enable fast and low-power neuromorphic computing. Memristive devices show excellent performance on emulating spiking neurons and synapses in hardware. However, the neuron circuits used for implementing a fully memristive TC SNN are absent. In this work, for the first time, we demonstrate a LIF neuron based on a NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> device to meet the requirements for the hardware implementation of TC SNNs. The neuron fires at most one spike within an inference window, and its spiking latency inverse to the input current intensity. Using such a neuron, we further experimentally demonstrated a fully memristive TC SNN (256 × 5) to recognize the Olivetti face patterns. Attributing to the one-spike scheme, the TC SNN achieves a sparser spiking number (~ 72 × reductions), faster inference speed (> 1.5 × improvement), lower power (~ 53 × reductions) than what happens in rate-coding SNNs.