SpikingJelly:一个开源的机器学习基础设施平台,用于基于尖端的情报
Wei Fang1,2,3, Yanqi Chen1,2, Jianhao Ding1
1School of Computer Science, Peking University, China.
Science advances
|October 6, 2023
概括
尖端神经网络 (SNN) 提供节能,以大脑为灵感的AI. SpikingJelly框架加速了SNN在神经形态硬件上的训练和部署,从而实现了先进的机器智能.
科学领域:
- 神经形态计算是一种神经形态计算.
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 尖端神经网络 (SNN) 模拟大脑功能,在神经形态硬件上进行节能计算.
- 现有的深度学习框架难以满足SNN的独特需求,包括自动区分和并行处理.
研究的目的:
- 引入SpikingJelly框架,这是一个全面的工具包,用于开发和部署Spiking神经网络.
- 解决SNN培训,优化和硬件部署当前框架的局限性.
主要方法:
- 开发了一个全工具包用于神经形态数据集预处理,SNN构建,参数优化和芯片部署.
- 实现了自动区分,并行计算加速和SNN工作流程高度集成的功能.
主要成果:
- 与现有方法相比,在深度SNN训练中实现了11倍的加速.
- 证明了卓越的可扩展性和灵活性,以加速定制SNN模型,降低成本.
结论:
- 斯派金杰利为合成节能SNN机器智能系统提供了强大的解决方案.
- 该框架通过促进SNN的开发和部署,大大推进了神经形态计算领域.
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