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

• URL: http://arxiv.org/abs/2404.01685v3
• Date: Sun, 08 Dec 2024 08:29:56 GMT
• Title: SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems
• Authors: Rachmad Vidya Wicaksana Putra, Muhammad Shafique,
• Abstract summary: Spiking Neural Networks (SNNs) can offer ultra-low power/energy consumption for machine learning-based application tasks.<n>Currently, most of the SNN architectures need a significantly larger model size to achieve higher accuracy.<n>We propose Spi Kernel, a novel methodology that improves the accuracy of SNNs through kernel size exploration.
• Score: 6.006032394972252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking Neural Networks (SNNs) can offer ultra-low 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 trade-offs 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 10M parameters and up to 4.8x speed-up of searching time, thereby making it suitable for embedded applications.

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