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Progressive compression and weight reinforcement for spiking neural networks

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.