高效和可扩展的TrioN (3N0C) 突触细胞用于模拟过程内存.
Junyoung Choi1, Byoungwoo Lee1, Jinho Byun1
1Department of Materials Science and Engineering, Postech, Pohang, 37673, Republic of Korea. kimseyoung@postech.ac.kr.
Materials horizons
|June 25, 2025
概括
使用无形的氧化 (a-IGZO) 的新型无电容突触装置 (TrioN) 可实现高效的神经形态计算. 这项技术为神经网络提供了高密度,快速切换和更高的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 电气工程 电气工程
背景情况:
- 非挥发性内存 (NVM) 交点数组对于模拟过程内存 (aPIM) 神经形态架构至关重要.
- 对于NVM设备的不对称性,需要基于电容器的突触细胞,从而增加了复杂性.
- 现有的解决方案面临密度,制造和能源效率方面的挑战.
研究的目的:
- 介绍一个新的,无电容的突触细胞用于神经形态计算.
- 为了利用无形的氧化 (a-IGZO) 来提高设备性能.
- 为了展示高密度神经形态数组的简化制造过程.
主要方法:
- 使用a-IGZO开发了一个3-NMOS 0-电容器 (TrioN,3N0C) 交点装置.
- 制造的硬件来证明选择性更新和切换特征.
- 在MNIST数据集的多层感知器 (MLP) 上使用随机梯度下降 (SGD) 和Tiki-Taka算法版本1 (TTv1) 进行神经网络模拟.
主要成果:
- TrioN 呈现出完美的对称性,高开/关比,以及超快速的 10 ns 切换.
- 制造硬件通过2周期更新实现了精确的选择性更新,提高了速度和能源效率.
- 在MNIST数据集上实现了96.89% (SGD) 和97.19% (TTv1) 的高精度.
结论:
- TrioN为神经形态计算提供了一个紧,节能和可扩展的解决方案.
- 无电容设计简化了制造过程,并增加了阵列密度.
- 基于a-IGZO的TrioN设备显示出下一代AI硬件的巨大潜力.
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