Adaptive neuron pruning based on hybrid coding of temporal and rate

Abstract

Spiking neural networks have the nature of high efficiency, energy saving, and bio-interpretability. They communicate through sparse and asynchronous spikes, so they have received extensive attention in the field of neuromorphic engineering and brain-like computing. At present, the commonly used encoding methods are mainly single-rate encoding and temporal encoding. However, rate encoding cannot make use of the time information in the spike train, which has high energy consumption. Temporal encoding limits the computing power of neurons and will produce dead neurons. Moreover, it is critical to find effective solutions that reduce network complexity and improve energy efficiency while maintaining high accuracy. Therefore, we propose a hybrid coding method based on rate coding and temporal coding to solve the limitation of single coding. We propose an adaptive online pruning strategy based on hybrid coding. In this pruning strategy, 100 neurons are pruned out of the 200-neuron network, which reduces the network size and obtains a more compact network structure. The memory capacity is reduced by 1.9×, the energy efficiency is increased by 2.4×, and the classification accuracy is reduced by less than 0.5%.