德尔格拉德:精确的基于事件的梯度,用于训练延迟和重量增加的神经形态硬件
Julian Göltz1,2, Jimmy Weber3, Laura Kriener4,5
1Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany. julian.goeltz@kip.uni-heidelberg.de.
Nature communications
|September 9, 2025
概括
我们介绍了DelGrad,这是一种针对尖端神经网络 (SNN) 的新培训方法,可以优化可训练的传输延迟. 这种基于事件的方法提高了神经形态硬件的准确性和效率.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 神经形态工程的神经形态工程
背景情况:
- 尖端神经网络 (SNN) 使用信号定时进行信息处理.
- 在SNN中可训练的传输延迟提高了准确性和效率.
- 目前对有延误的SNNs的培训方法不准确,资源密集.
研究的目的:
- 为具有可训练的延迟的SNNs开发一种分析,基于事件的培训方法.
- 为了计算突触重量和传输延迟的确切损失梯度.
- 为了在神经形态硬件上实现高效的SNN培训.
主要方法:
- 提出了DelGrad,一个基于事件的分析训练算法.
- 基于纯粹的尖峰时间计算了精确的损失梯度.
- 在BrainScaleS-2神经形态平台上实现了DelGrad.
主要成果:
- 演示了DelGrad优化重量和延迟的能力.
- 展示了SNN带延迟的参数效率和准确性的好处.
- 第一次通过实验验证了延迟对杂硬件的稳定效应.
结论:
- 德尔格拉德提供了一种精确而有效的方法,用于训练有延迟的SNN.
- 该方法消除了对内部变量跟踪的需求,适合神经形态硬件.
- 德尔格拉德显著推进了对神经形态基质的SNN培训.
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