  Larger Spiking Neural Network (SNN) models are typically favorable as they
can offer higher accuracy. However, employing such models on the resource- and
energy-constrained embedded platforms is inefficient. Towards this, we present
a tinySNN framework that optimizes the memory and energy requirements of SNN
processing in both the training and inference phases, while keeping the
accuracy high. It is achieved by reducing the SNN operations, improving the
learning quality, quantizing the SNN parameters, and selecting the appropriate
SNN model. Furthermore, our tinySNN quantizes different SNN parameters (i.e.,
weights and neuron parameters) to maximize the compression while exploring
different combinations of quantization schemes, precision levels, and rounding
schemes to find the model that provides acceptable accuracy. The experimental
results demonstrate that our tinySNN significantly reduces the memory footprint
and the energy consumption of SNNs without accuracy loss as compared to the
baseline network. Therefore, our tinySNN effectively compresses the given SNN
model to achieve high accuracy in a memory- and energy-efficient manner, hence
enabling the employment of SNNs for the resource- and energy-constrained
embedded applications.
