Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking Neural Networks (SNNs) in this domain is hampered by their inherent sequential computational dependency. This constraint arises from the need for each time step's processing to rely on the preceding step's outcomes, significantly impeding the adaptability of SNN models to massively parallel computing environments. Addressing this challenge, our paper introduces the innovative Parallel Spiking Unit (PSU) and its two derivatives, the Input-aware PSU (IPSU) and Reset-aware PSU (RPSU). These variants skillfully decouple the leaky integration and firing mechanisms in spiking neurons while probabilistically managing the reset process. By preserving the fundamental computational attributes of the spiking neuron model, our approach enables the concurrent computation of all membrane potential instances within the SNN, facilitating parallel spike output generation and substantially enhancing computational efficiency. Comprehensive testing across various datasets, including static and sequential images, Dynamic Vision Sensor (DVS) data, and speech datasets, demonstrates that the PSU and its variants not only significantly boost performance and simulation speed but also augment the energy efficiency of SNNs through enhanced sparsity in neural activity. These advancements underscore the potential of our method in revolutionizing SNN deployment for high-performance parallel computing applications.

翻译：高效的并行计算已成为推动人工智能发展的关键因素。然而，脉冲神经网络（SNN）在此领域的应用受限于其固有的顺序计算依赖。这一约束源于每个时间步的处理需依赖前一步的结果，严重阻碍了SNN模型适应大规模并行计算环境的能力。针对这一挑战，本文创新性地提出了并行脉冲单元（PSU）及其两种衍生变体——输入感知型PSU（IPSU）和重置感知型PSU（RPSU）。这些变体巧妙地将脉冲神经元中的漏积分与发放机制解耦，同时概率性地管理重置过程。通过保留脉冲神经元模型的基本计算属性，我们的方法能够使SNN内所有膜电位实例实现并发计算，促进并行脉冲输出生成，并显著提升计算效率。在包括静态图像、时序图像、动态视觉传感器（DVS）数据以及语音数据集等各类数据集上的全面测试表明，PSU及其变体不仅显著提升了性能和模拟速度，还通过增强神经活动稀疏性提高了SNN的能效。这些进展彰显了我们的方法在革新SNN用于高性能并行计算应用中的潜力。