让脉冲神经网络并行计算，提升效率且不损失动态特性
Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural Networks
- 通过膜电位估计实现脉冲神经元的并行计算
- 在高神经元密度下仍保持最优精度与效率
- 适合部署在类脑芯片等硬件加速场景
脉冲神经网络（SNN）的计算效率低主要源于膜电位的串行更新，这一问题在长编码周期下尤为突出。为解决此问题，我们提出膜电位估计并行脉冲神经元（MPE-PSN），一种支持并行计算的脉冲神经元方法，在保持SNN固有动态特性的前提下显著提升计算效率。实验表明，该方法在类脑数据集上达到当前最优（SOTA）的准确率与效率表现。代码已开源。
原文摘要 · Abstract (English)
The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at~\url{https://github.com/chrazqee/MPE-PSN}. \end{abstract}
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