Frame-Unit Operating Neuron Circuits for Hardware Recurrent Spiking Neural Networks

Abstract

A frame-unit operating neuron circuit (f-NC) for hardware recurrent spiking neural networks (RSNNs) is proposed. The proposed f-NC enables the two essential features required in RSNNs, which have been challenging to implement in conventional integrate-and-fire (I&F) neuron-based systems: 1) the ability to recurrently feed the output from the previous state ( ${t} -1$ ) as input to the current state (t) in the frame unit, and 2) the implementation of a $\tanh $ activation function. System-level simulations of the Free Spoken Digits Dataset are performed to confirm the operation of RSNNs with f-NCs with charge-trap flash (CTF)-based and-type synaptic arrays, which store 16-state weights and operate array- and circuit-level vector-matrix multiplication (VMM). It shows 97.05% RSNN inference accuracy, including quantized synaptic weight and nonidealities in the activation function of the neuron circuit.