一个具有记忆性的突触电路和优化算法用于突触控制
Seda Günakın1, Zehra Gülru Çam Taşkıran1
1Electronics and Communication Engineering Department, Yildiz Technical University, 34220 Istanbul, Turkey.
Cognitive neurodynamics
|May 16, 2025
概括
本研究介绍了一种优化方法,以实现机器学习的memristor交叉条数组中的线性重量控制. 这克服了非线性挑战,使得高效的在线培训能够使用具有记忆力的设备.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 电气工程 电气工程
背景情况:
- 机器学习中的反向传播训练需要线性重量变化,以便直接应用于memristor交叉阵列.
- 非线性memristance及其时间不稳定性对直接训练构成记忆和能量挑战.
- 现有的方法需要复杂的算法或内存来处理memristor非线性.
研究的目的:
- 为机器学习开发一种方法,在memristor电路中实现线性重量控制.
- 为了克服与非线性记忆阻抗相关的记忆和能量缺陷.
- 为了使反向传播训练能够直接应用于memristor交叉杆阵列.
主要方法:
- 利用一种优化方法,使用电荷控制和流量控制的memristor方程.
- 采用人工蜂群算法来确定电路参数和控制信号持续时间.
- 专注于实现对正负权重的重量变化进行线性控制.
主要成果:
- 实现了重量变化的线性控制,灵敏度高达0.02.
- 实验重量控制显示平均平方误差为2.33×10−4.
- 实现了 98.186% 的基于软件的测试准确度跟踪率.
结论:
- 拟议的优化方法和成本函数使得使用memristor元素进行在线培训的线性控制成为可能.
- 这种方法简化了重量控制,解决了基于memristor的机器学习中的非线性问题.
- 这些发现为在memristor硬件上更有效,更直接地应用机器学习培训铺平了道路.
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