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记忆器交叉杆电路实现平衡传播,用于设备上的学习
Seokjin Oh1, Jiyong An1, Seungmyeong Cho1
1School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Micromachines
|July 29, 2023
概括
本研究介绍了一种用于平衡传播 (EP) 的新型模拟电路,可以在边缘智能硬件中实现实际的设备内学习. 新设计通过同时计算解决方案并使用修改后的学习规则来简化实现.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能 硬件 硬件
- 机器学习算法 机器学习算法
背景情况:
- 均衡传播 (EP) 是神经网络的本地学习算法.
- 数字EP实现对边缘智能硬件具有挑战性,原因是代计算.
- 现有的EP模拟电路解决方案面临诸如内存要求和外围电路复杂性等挑战.
研究的目的:
- 提出一种新的模拟电路技术,以实现EP算法的实际和可实施的实现.
- 为了解决EP之前模拟电路实现的局限性.
- 为了使在设备上的学习在边缘智能硬件使用EP.
主要方法:
- 开发了模拟电路,同时计算自由相和推相解决方案,消除了对自由相解决方案存储器的需求.
- 提出并实施了一种简化的EP学习规则,该规则基于每次编程脉冲的固定行为变化.
- 模拟和验证了一个memristor导电性更新电路,用于在memristor横杆上进行突触权力训练.
主要成果:
- 拟议的模拟电路技术成功地消除了存储自由相溶液的需要.
- 修改后的EP学习规则可以在没有复杂的验证的情况下实现实用和可实现的重量更新电路.
- 模拟证实了memristor导电更新电路对训练突触重量的有效性.
结论:
- 新型模拟电路技术为实现平衡传播算法在硬件中提供了实用和可实现的解决方案.
- 这一进步促进了对边缘智能应用程序至关重要的设备内学习能力.
- 简化学习规则和同时计算解决方案克服了关键的实施障碍.
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