用于模式识别的振荡神经网络学习:在芯片上的学习视角和实现
Madeleine Abernot1, Nadine Azemard1, Aida Todri-Sanial1,2
1Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Department of Microelectroncis, University of Montpellier, CNRS, Montpellier, France.
Frontiers in neuroscience
|July 3, 2023
概括
本研究介绍了使用振荡神经网络 (ONN) 的AI芯片上的持续学习. 它在数字ONN设计中使用Hebbian和Storkey规则展示了高效的无监督学习.
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
- 机器学习 机器学习
背景情况:
- 人类大脑的学习是持续的,与当前人工智能的预训练,非进化模型形成鲜明对比.
- 人工智能模型面临着不断变化的环境和数据,需要持续学习算法.
- 直接在芯片上实施持续学习是一个重大挑战.
研究的目的:
- 调查在芯片上的持续学习算法的实现.
- 将霍普菲尔德神经网络 (HNN) 的无监督学习规则调整为振荡神经网络 (ONN).
- 提出和验证数字ONN设计,用于无监督的芯片上学习.
主要方法:
- 专注于振荡神经网络 (ONN) 作为神经形态计算范式.
- 研究了HNN无监督学习规则 (Hebbian和Storkey) 对ONN的适应性.
- 开发了一个数字ONN架构,用于芯片上实现.
主要成果:
- 证明了拟议数字ONN架构的有效的芯片上学习能力.
- 成功实施了使用Hebbian和Storkey规则的无监督学习.
- 达到了数百微秒的学习时间,用于多达35个数字振荡器的网络.
结论:
- 拟议的数字ONN设计可以实现有效的芯片上的持续学习.
- 这项工作为在神经形态系统中实施无监督学习提供了一个可行的解决方案.
- 这些发现为更具适应性和进化的AI模型铺平了道路.
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