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.

翻译：深度神经网络在各领域已被证明是高效工具，但其计算和内存成本限制了它们在便携设备上的广泛部署。近年来边缘计算设备的快速增长推动了针对机器学习框架上述局限性的技术探索。人工神经网络（ANN）的量化技术通过将全精度突触权重转换为低位版本，成为解决方案之一。与此同时，脉冲神经网络（SNN）因其时间信息处理能力、能效优势及高生物合理性，已成为传统ANN的极具吸引力的替代方案。尽管两者源于相同动机，但二者联合应用的研究仍不充分。因此，本文旨在弥合量化神经网络与SNN领域最新进展之间的鸿沟，系统研究了以Sigmoid函数线性组合表征的量化函数在SNN低位权重量化中的性能表现。该量化函数在CIFAR10-DVS、DVS128 Gesture、N-Caltech101和N-MNIST四个基准测试中，针对二值网络分别取得了64.05%、95.45%、68.71%和99.43%的当前最优性能，在仅产生微小精度损失的同时实现高达31倍的内存节省，显著优于现有方法。