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对于尖端神经网络的稀有发射规范化方法,采用时间到第一个尖端编码.

Yusuke Sakemi1,2, Kakei Yamamoto3, Takeo Hosomi4

  • 1Research Center for Mathematical Engineering, Chiba Institute of Technology, Narashino, Japan. yusuke.sakemi@p.chibakoudai.jp.

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概括

本研究引入了两种新的规范化方法,用于降低使用时间到第一个尖峰 (TTFS) 编码的尖峰神经网络 (SNN) 的发射频率. 这些方法提高了SNN信息处理的能源效率.

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科学领域:

  • 人工智能的人工智能
  • 计算神经科学是一种神经科学.
  • 机器学习 机器学习

背景情况:

  • 尖端神经网络 (SNN) 显示了节能计算的前景.
  • 训练SNN以错误反向传播,特别是使用发射时间,正在推进.
  • 时间到第一个尖峰 (TTFS) 编码可以实现低发射频率,但在非常低的速度下,其全部潜力尚未开发.

研究的目的:

  • 研究和增强TTFS编码的SNN在降低发射频率下的信息处理能力.
  • 引入新的规范化技术,以进一步降低SNN的火速.
  • 在标准图像数据集上评估这些方法的有效性.

主要方法:

  • 提出了两种基于尖峰时间的稀疏发射 (SSR) 规范化方法.
  • 这些方法只使用发射时间和相关的重量.
  • 在使用MLP和CNN架构的MNIST,时尚-MNIST和CIFAR-10数据集上评估方法.

主要成果:

  • 证明了SSR规范化在减少TTFS编码SNN的发射频率方面的有效性.
  • 研究了降低发射速率对不同数据集和网络结构SNN性能的影响.
  • 证实了在不显著降低性能的情况下实现更低的发射频率的可行性.

结论:

  • 拟议的SSR规范化方法有效地降低了TTFS编码的SNN中的发射频率.
  • 这些技术有助于提高SNN的能源效率.
  • 对于先进的神经形态计算应用,需要对低频SNN进行进一步的研究.