Python-LTspice Co-Simulation to Train Neural Networks with Memristive Synapses to Learn Logic Gate Operations

Abstract

Neuromorphic computing attempts to mimic the neural architecture of human brain by delivering a non vonNeumann hardware which can run even the most complex artificial intelligence algorithms at extremely fast computational speeds at power requirement as low in order as few tens of watts just like the human brain does. Since the brain is a complex mesh of millions of neurons communicating via the synapses and spiking signals in between them, there is a requirement of a circuit based memory element which can play the role of these synapses in electronic circuits. The memristors with there unique pinched hysteresis property have been proposed and modelled to act as these synapses. This paper introduces LTspice modelling of a simple artificial neural network with memristive synapses and training it for the universal gates-NOR and NAND by providing a mechanism for interpreting the compressed binary data generated by parametric LTspice simulations. The results show potential for application in many other crucial neuromorphic simulations and their numeric interpretation using the tool developed for Co-simulation of LTspice with the open source programming language, Python.