NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks

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Abstract
Spiking neural networks (SNNs) are an alternative computational paradigm to artificial neural networks (ANNs) that have attracted attention due to their event-driven execution mechanisms, enabling extremely low energy consumption. However, a significant challenge and opportunity in SNNs is to optimize memory and compute costs while maintaining accuracy, thereby further reducing energy consumption. Model quantization has been proposed as a promising technique to improve the running efficiency via the number of data bits reduction. Whereas, this technique has yet to be well studied in the neuromorphic computing domain. The underlying reason is that the behaviors of SNNs are quite different from those of ANNs, making 1) the accuracy of SNNs usually sensitive to data precision, and 2) the introduction of a temporal dimension to characterize neuronal dynamics. In this paper, we present NeuronQuant, an accurate and energy-efficient quantization framework to reduce the precision of neurons while maintaining accuracy. The key insight is to design a post-training quantization method guided by the activity of neurons, efficiently reducing the bit-width of parameters based on local relationships within neurons. Additionally, a budget-aware mixed bit-width allocation strategy for the total model size enables the adaptive growth and narrowing of precision in each layer, leading to a mixed-precision quantization scheme of the desired size. Extensive evaluations demonstrate that NeuronQuant can achieve a compressed SNN with 5.2 bits on average and 1.51× power consumption reduction with a superior model accuracy which is quite impressive for SNN. Code is released at https://github.com/shieldforever/NeuronQuant.
Funding Information
  • National Natural Science Foundation of China (62402311)