开放循环模拟可编程电化学内存阵列
Peng Chen1, Fenghao Liu1, Peng Lin2,3
1College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Nature communications
|October 4, 2023
概括
本研究引入了一种新的电化学记忆 (ECRAM) 阵列,用于神经网络中高效的模拟编程. 开发的ECRAM能够准确地在现场训练人工智能硬件,实现高分类准确度.
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:
- 新兴记忆为神经网络提供低功耗的模拟计算.
- 设备的非理想性限制了内存阵列中准确的模拟编程.
- 这阻碍了人工智能 (AI) 的现场培训应用.
研究的目的:
- 为了展示一个被动电化学记忆 (ECRAM) 阵列,用于准确的模拟编程.
- 为了使神经网络能够使用新兴的内存硬件进行有效的现场训练.
- 为了克服AI硬件中设备非理想性所带来的局限性.
主要方法:
- 开发了一种被动电化学记忆 (ECRAM) 阵列.
- 使用开放循环,串行方法将图像模式编程到ECRAM阵列中.
- 在现场训练ECRAM阵列上的双层神经网络.
- 模拟训练用于像VGG-8这样的大规模神经网络.
主要成果:
- 在没有反调整的情况下实现了高编程精度.
- 证明了出色的开环模拟可编程性.
- 达到99.4%的软件类型分类准确度,用于毒检测.
- 通过模拟验证了对大规模神经网络的训练能力.
结论:
- ECRAM阵列为准确的模拟编程提供了一个可行的解决方案.
- 这项技术促进了神经网络在人工智能硬件中的现场训练.
- 在新兴的基于内存的AI系统中实现学习功能的新途径.
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