  Spiking Neural Networks (SNNs) can offer ultra low power/ energy consumption
for machine learning-based applications 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 a novel methodology that improves the accuracy of SNNs through
kernel size scaling. Its key steps include investigating the impact of
different kernel sizes on the accuracy, devising new sets of kernel sizes,
generating SNN architectures based on the selected kernel sizes, and analyzing
the accuracy-memory trade-offs for SNN model selection. The experimental
results show that our methodology achieves higher accuracy than
state-of-the-art (93.24% accuracy for CIFAR10 and 70.84% accuracy for CIFAR100)
with less than 10M parameters and up to 3.45x speed-up of searching time,
thereby making it suitable for embedded applications.
