一个自我训练的尖超导神经形态架构.
M L Schneider1, E M Jué2,3, M R Pufall1
1Applied Physics Division, National Institute of Standards and Technology, Boulder, CO 80305 USA.
Npj unconventional computing
|March 7, 2025
概括
本研究介绍了用于训练神经形态计算硬件的新的局部强化学习规则. 这些规则允许在超导芯片上直接进行快速,纳秒级的学习,而无需明确的重量编程.
科学领域:
- 神经形态计算是一种神经形态计算.
- 人工智能硬件是人工智能的硬件.
- 超导电子产品的超导电子
背景情况:
- 神经形态计算旨在模仿大脑以提高效率.
- 训练神经形态硬件是具有挑战性的,因为在典型的算法中需要全球信息.
- 目前的模拟实现在明确权重编程方面遇到了困难.
研究的目的:
- 为神经形态系统开发高效,硬件可实现的训练规则.
- 在神经形态硬件中展示一种用于局部重量更新的新方法.
- 克服模拟神经网络实施中的关键挑战.
主要方法:
- 开发了基于强化学习的局部权重更新规则.
- 在使用SPICE电路模拟的超导硬件中实现了这些规则.
- 创建了一个能够进行芯片内学习的小规模神经网络.
主要成果:
- 每次更新的学习时间大约为1纳秒.
- 通过调整目标输出来证明网络学习新功能的能力.
- 消除了对预先编程的明确重量值的需求.
结论:
- 拟议的本地学习规则对于神经形态硬件培训是有效的.
- 这种方法大大简化了模拟神经网络的实施.
- 开发的系统为先进的,自我学习的神经形态设备提供了一个有前途的方向.
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