Conversion of analog to spiking neural networks using sparse temporal coding

The activations of an analog neural network (ANN) are usually treated as representing an analog firing rate. When mapping the ANN onto an equivalent spiking neural network (SNN), this rate-based conversion can lead to undesired increases in computation cost and memory access, if firing rates are high. This work presents an efficient temporal encoding scheme, where the analog activation of a neuron in the ANN is treated as the instantaneous firing rate given by the time-to-first-spike (TTFS) in the converted SNN. By making use of temporal information carried by a single spike, we show a new spiking network model that uses 7-10× fewer operations than the original rate-based analog model on the MNIST handwritten dataset, with an accuracy loss of <; 1%.