Progressive compression and weight reinforcement for spiking neural networks. (2nd March 2022) - Elbez, Hammouda; Kamel Benhaoua, Mohammed; Devienne, Philippe; Boulet, Pierre
Summary: Neuromorphic architectures are one of the most promising architectures to reduce the energy consumption of tomorrow's computers. These architectures are inspired by the behavior of the brain at a fairly precise level and consist of artificial spiking neural networks. To optimize the implementation of these architectures, we propose in this article a novel progressive network compression and reinforcement technique. This technique consists of two functions: progressive pruning and dynamic synaptic weight reinforcement, which we apply after each training batch. The proposed approach delivers a highly compressed network (up to 80% of compression rate) while preserving the network performance when tested with MNIST.