SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems

Spiking neural networks (SNNs) can offer ultralow power/energy consumption for machine learning-based application tasks due to their sparse spike-based operations. Currently, most of the SNN architectures need a significantly larger model size to achieve higher accuracy, which is not suitable for resource-constrained embedded applications. Therefore, developing SNNs that can achieve high accuracy with acceptable memory footprint is highly needed. Toward this, we propose SpiKernel, a novel methodology that improves the accuracy of SNNs through kernel size exploration. Its key steps include: 1) investigating the impact of different kernel sizes on the accuracy; 2) devising new sets of kernel sizes; 3) generating SNN architectures using neural architecture search based on the selected kernel sizes; and 4) analyzing the accuracy-memory tradeoffs for SNN model selection. The experimental results show that our SpiKernel achieves higher accuracy than state-of-the-art works (i.e., 93.24% for CIFAR10, 70.84% for CIFAR100, and 62% for TinyImageNet) with less than 10 M parameters and up to 4.8x speed-up of searching time, thereby making it suitable for embedded applications.