Implementation of Direct Feedback Alignment Through Time Algorithm in Training of Shallow Fully Memristive Spiking Neural Networks

ABSTRACT In this paper, we present shallow architectures of memristive spiking neural networks (MSNNs) with a single hidden layer and a simple memristive leaky integrate‐and‐fire (LIF) neuron. The SPICE‐based model is used to describe the response of a volatile memristor within LIF, whose intrinsic reset to the initial state eliminates the need for additional electronic components for discharging the membrane capacitance. The direct feedback alignment through time (DFATT) algorithm was implemented for the first time for the training of MSNNs. The deployment of the DFATT learning algorithm on two MSNN architectures resulted in average training times per epoch that were reduced by factors of 2.1 and 2.7 compared to equivalent architectures trained with the backpropagation through time (BPTT) algorithm, with comparable accuracy on MNIST and FMNIST datasets. The reported results indicate the possibility of combining memristive technology with the bioplausible DFATT algorithm toward building energy‐efficient hardware.