  Deep neural networks have been proven to be highly effective tools in various
domains, yet their computational and memory costs restrict them from being
widely deployed on portable devices. The recent rapid increase of edge
computing devices has led to an active search for techniques to address the
above-mentioned limitations of machine learning frameworks. The quantization of
artificial neural networks (ANNs), which converts the full-precision synaptic
weights into low-bit versions, emerged as one of the solutions. At the same
time, spiking neural networks (SNNs) have become an attractive alternative to
conventional ANNs due to their temporal information processing capability,
energy efficiency, and high biological plausibility. Despite being driven by
the same motivation, the simultaneous utilization of both concepts has yet to
be thoroughly studied. Therefore, this work aims to bridge the gap between
recent progress in quantized neural networks and SNNs. It presents an extensive
study on the performance of the quantization function, represented as a linear
combination of sigmoid functions, exploited in low-bit weight quantization in
SNNs. The presented quantization function demonstrates the state-of-the-art
performance on four popular benchmarks, CIFAR10-DVS, DVS128 Gesture,
N-Caltech101, and N-MNIST, for binary networks (64.05\%, 95.45\%, 68.71\%, and
99.43\% respectively) with small accuracy drops and up to 31$\times$ memory
savings, which outperforms existing methods.
