Robust Mapping of a Software‐Trained Adiabatic Capacitive Artificial Neuron

ABSTRACT The adiabatic capacitive artificial neuron (ACAN) has been previously shown to offer the potential for ultra‐low power computation in full custom analogue ASIC designs. However, it did not consider how a real‐world, software‐trained, artificial neuron (AN) could be mapped robustly onto the circuit. In this paper, we describe how an AN, with positive‐valued weights, bias and a binary activation function, can be mapped directly and precisely onto an ACAN. The functional equivalence and properties of the mapping are demonstrated with ANs extracted from an artificial neural network (ANN) trained against a binarized MNIST dataset using an open‐source software AI framework.