Sparsity Through Spiking Convolutional Neural Network for Audio Classification at the Edge

Convolutional neural networks (CNNs) have shown to be effective for audio classification. However, deep CNNs can be computationally heavy and unsuitable for edge intelligence as embedded devices are generally constrained by memory and energy requirements. Spiking neural networks (SNNs) offer potential as energy-efficient networks but typically underperform typical deep neural networks in accuracy. This paper proposes a spiking convolutional neural network (SCNN) that exhibits excellent accuracy of above 98 % on a multi-class audio classification task. Accuracy remains high with weight quantization to INT8-precision. Additionally, this paper examines the role of neuron parameters in co-optimizing activation sparsity and accuracy.