Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their complex neuronal dynamics and sparse binary activations (spikes) inspired by the biological neural system. Traditional neuron models use iterative step-by-step dynamics, resulting in serial computation and slow training speed of SNNs. Recently, parallelizable spiking neuron models have been proposed to fully utilize the massive parallel computing ability of graphics processing units to accelerate the training of SNNs. However, existing parallelizable spiking neuron models involve dense floating operations and can only achieve high long-term dependencies learning ability with a large order at the cost of huge computational and memory costs. To solve the dilemma of performance and costs, we propose the mul-free channel-wise Parallel Spiking Neuron, which is hardware-friendly and suitable for SNNs' resource-restricted application scenarios. The proposed neuron imports the channel-wise convolution to enhance the learning ability, induces the sawtooth dilations to reduce the neuron order, and employs the bit-shift operation to avoid multiplications. The algorithm for the design and implementation of acceleration methods is discussed extensively. Our methods are validated in neuromorphic Spiking Heidelberg Digits voices, sequential CIFAR images, and neuromorphic DVS-Lip vision datasets, achieving superior performance over SOTA spiking neurons. Training speed results demonstrate the effectiveness of our acceleration methods, providing a practical reference for future research. Our code is available at \href{https://github.com/PengXue0812/Multiplication-Free-Parallelizable-Spiking-Neurons-with-Efficient-Spatio-Temporal-Dynamics}{Github}.

翻译：脉冲神经网络（SNNs）区别于人工神经网络（ANNs），其特点在于受生物神经系统启发的复杂神经元动力学和稀疏二元激活（脉冲）。传统神经元模型采用迭代逐步动力学，导致SNNs的计算呈串行且训练速度缓慢。近年来，研究者提出了可并行化的脉冲神经元模型，以充分利用图形处理器的大规模并行计算能力来加速SNNs的训练。然而，现有的可并行化脉冲神经元模型涉及密集浮点运算，且仅能通过高阶设计实现较强的长期依赖学习能力，但代价是巨大的计算和内存开销。为解决性能与成本之间的困境，我们提出了一种免乘法通道级并行脉冲神经元，该模型对硬件友好，适用于SNNs资源受限的应用场景。所提出的神经元引入通道级卷积以增强学习能力，采用锯齿状扩张以降低神经元阶数，并利用位移操作避免乘法运算。本文详细讨论了设计与加速方法的算法实现。我们的方法在神经形态Spiking Heidelberg Digits语音、序列CIFAR图像以及神经形态DVS-Lip视觉数据集上进行了验证，其性能优于当前最先进的脉冲神经元模型。训练速度结果证明了我们加速方法的有效性，为未来研究提供了实用参考。我们的代码发布于\href{https://github.com/PengXue0812/Multiplication-Free-Parallelizable-Spiking-Neurons-with-Efficient-Spatio-Temporal-Dynamics}{Github}。