一个基于memristor的学习引擎,用于基于synaptic的线上学习
IEEE transactions on biomedical circuits and systems
|June 30, 2023
概括
这项研究引入了一种用于大脑启发的尖端神经网络 (SNN) 的新型学习引擎,该引擎能够实现复杂的基于痕迹的学习规则,如尖端时间依赖可塑性 (STDP) 和贝叶斯信任传播神经网络 (BCPNN),具有显著的能源效率.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 记忆器对于突触在线学习在尖端神经网络 (SNN) 中至关重要.
- 现有的memristor方法与复杂的基于痕迹的学习规则 (如STDP和BCPNN) 进行斗争.
- 需要高效的硬件实现先进的SNN学习算法.
研究的目的:
- 提出和实现一种新的学习引擎,用于SNN的基于线上学习.
- 通过使用memristors实现复杂的学习规则,包括STDP和BCPNN.
- 为了证明拟议的学习引擎的能源效率和性能.
主要方法:
- 开发了一个混合学习引擎,结合基于memristor和模拟计算块.
- 利用memristors通过它们的非线性物理性质来模拟突触痕迹动态.
- 集成模拟计算用于诸如加法,乘法,对数和整合之类的基本操作.
- 设计了一个可重新配置的引擎来模拟STDP和BCPNN学习规则.
主要成果:
- 实现了低能耗:STDP的10.61 pJ/突触更新和BCPNN的51.49 pJ/突触更新.
- 与ASIC同行相比,显著减少了能量 (147.03×和93.61×为180nm;9.39×和5.63×为40nm).
- 在能源效率方面,STDP和BCPNN的性能分别超过了先进的神经形态平台 (Loihi,eBrainII) 的11.31×和13.13×.
结论:
- 拟议的学习引擎有效地在SNN中实现基于痕迹的STDP和BCPNN学习规则.
- 这种架构与现有的ASIC和神经形态解决方案相比,可以大幅节省能源.
- 基于memristor的设计为更高效和更强大的大脑启发的计算系统铺平了道路.
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