与CMOS兼容的闪光门式基于thyristor的神经形态模块,面积小,能耗低,用于内存计算
Jonghyun Ko1, Jiseong Im1, Jangsaeng Kim1,2,3
1Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
Science advances
|July 18, 2025
概括
这项研究介绍了一种基于激光电阻的新型神经形模块 (FGTNM),用于高效的内存计算 (IMC). FGTNM集成了多个神经网络功能,实现高级计算应用的高精度和低能耗.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 传统的·诺伊曼架构面临性能瓶.
- 内存计算 (IMC) 旨在克服这些局限性,特别是在模拟向量矩阵乘法 (VMM) 中.
- 将神经形模块与内存集成至关重要,以扩大IMC功能.
研究的目的:
- 提出一种新的互补性金属氧化物半导体 (CMOS) 兼容的闪光门式基于thyristor的神经形态模块 (FGTNM).
- 为了证明FGTNM能够将多个神经网络功能 (量子化,非线性激活,最大聚合) 集成到单个模块中.
- 用FGTNM展示系统级的IMC性能.
主要方法:
- 开发一个CMOS兼容的FGTNM.
- 在单个晶圆上集成闪存,闪开式电阻,n型金属氧化物半导体和p型金属氧化物半导体设备.
- 评估FGTNM的足迹,能源消耗和CIFAR-10分类上的准确性.
主要成果:
- FGTNM的足迹很小 (53 μm2) 和能耗很低 (9.1 fJ/操作).
- 它的性能优于以前基于CMOS的神经形模块.
- 使用FGTNM的系统级IMC在CIFAR-10分类上实现了89.97%的准确性.
结论:
- FGTNM有效地整合了各种神经网络功能,从而实现了高效的IMC.
- 拟议的模块在尺寸和能源效率方面提供了卓越的性能.
- 这项工作突出了在VMM之外的更广泛的IMC应用中共同集成各种设备的潜力.
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