Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other fields and can also be applied to artificial intelligence, machine learning, and other fields. At present, many simulators using central processing unit (CPU) or graphics processing unit (GPU) have been developed. However, due to the randomness of connections between neurons and spiking events in SNN simulation, this causes a lot of memory access time. To alleviate this problem, we developed an SNN simulator SWsnn based on the new Sunway SW26010pro processor. The SW26010pro processor consists of six core groups, each with 16 MB of local data memory (LDM). LDM has the characteristics of high-speed read and write, which is suitable for performing simulation tasks similar to SNNs. Experimental results show that SWsnn runs faster than other mainstream GPU-based simulators when simulating a certain scale of neural network, showing a strong performance advantage. To conduct larger scale simulations, SWsnn designed a simulation computation based on a large shared model of Sunway processor and developed a multiprocessor version of SWsnn based on this mode, achieving larger scale SNN simulations.

中文翻译：

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SWsnn：一种新颖的尖峰神经网络模拟器。

尖峰神经网络（SNN）模拟器在神经系统建模和脑功能研究中发挥着重要作用。它们可以帮助科学家重现和探索大脑区域、神经科学、类脑计算等领域的神经元活动，也可以应用于人工智能、机器学习等领域。目前，已经开发了许多使用中央处理单元（CPU）或图形处理单元（GPU）的模拟器。然而，由于SNN模拟中神经元之间的连接和尖峰事件的随机性，这导致了大量的内存访问时间。为了缓解这个问题，我们开发了基于新的神威SW26010pro处理器的SNN模拟器SWsnn。SW26010pro 处理器由六个核心组组成，每个核心组具有 16 MB 本地数据存储器 (LDM)。LDM具有高速读写的特点，适合执行类似于SNN的仿真任务。实验结果表明，SWsnn在模拟一定规模的神经网络时，比其他主流的基于GPU的模拟器运行速度更快，表现出很强的性能优势。为了进行更大规模的仿真，SWsnn设计了基于Sunway处理器大型共享模型的仿真计算，并基于该模式开发了SWsnn的多处理器版本，实现了更大规模的SNN仿真。