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SWsnn:一个新型模拟器用于尖端神经网络

Zhichao Wang^1,2, Xuelei Li¹, Jianping Fan³

¹Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Journal of computational biology : a journal of computational molecular cell biology

|August 16, 2023

概括

一个新的尖端神经网络 (SNN) 模拟器,SWsnn,利用Sunway SW26010pro处理器的本地数据内存进行更快的模拟. 这种方法在某些网络尺度上优于基于GPU的模拟器,使得大规模的神经科学研究成为可能.

科学领域:

计算神经科学是一种计算神经科学.
人工智能的人工智能是人工智能.
高性能计算的高性能计算.

背景情况:

尖端神经网络 (SNN) 模拟器对于建模大脑功能和推进人工智能至关重要.
由于SNN模拟的性质,现有的基于CPU和GPU的模拟器面临着对内存访问时间的挑战.
孙威SW26010pro处理器提供高速本地数据存储器 (LDM),对SNN模拟有潜在的好处.

研究的目的:

开发一个新的尖端神经网络 (SNN) 模拟器,SWsnn,优化为Sunway SW26010pro处理器.
为了解决传统SNN模拟器的内存访问时间限制.
通过高效的硬件利用,实现更大规模的SNN模拟.

主要方法:

开发了SWsnn模拟器,使用了Sunway SW26010pro处理器及其每核心组的16MB本地数据存储器 (LDM).
基于Sunway处理器的大型共享模型实施了一个模拟计算策略.
创建了SWsnn的多处理器版本,以促进大规模的模拟.

主要成果:

与主流的基于GPU的模拟器相比,SWsnn在特定神经网络尺度上表现出更高的性能.
模拟器有效地利用了Sunway SW26010pro处理器的高速LDM进行SNN模拟任务.
多处理器版本成功实现了更大规模的尖端神经网络模拟.

结论:

在Sunway SW26010pro架构上,SWsnn模拟器为Spiking神经网络模拟提供了显著的性能优势.
这一发展为更广泛,更复杂的神经网络建模和大脑研究铺平了道路.
有效地使用本地数据内存是克服SNN模拟性能瓶的关键.

关键词:

在SW26010pro上使用.模拟模拟是指一个模拟模拟.尖的神经网络的神经网络.

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