线性导电更新改进了CMOS兼容的二阶记忆器,用于快速和节能地训练使用记忆器交叉杆阵列的神经网络
See-On Park1, Taehoon Park1, Hakcheon Jeong1
1The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. shinhyun@kaist.ac.kr.
Nanoscale horizons
|July 5, 2023
概括
研究人员改进了用于人工神经网络的memristor设备. 这提高了神经形态计算系统的精度和能源效率,为先进的AI硬件铺平了道路.
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
- 人工智能的人工智能
背景情况:
- 由于其简单的结构和高密度集成的潜力,memristors是人工神经网络的有希望的非易失性内存设备.
- 记忆性突触比传统的·诺伊曼计算架构提供了理论上的能源效率优势.
- 目前基于memristor交叉阵列的神经网络由于memristor非线性和不对称问题而表现出较低的准确性.
研究的目的:
- 为了提高CMOS兼容的基于HfO2的memristor中的脉冲更新的线性和对称性.
- 为了实现基于memristor交叉条阵列的神经网络的节能和准确训练.
主要方法:
- 使用了第二阶段的memristor效应,结合了加热脉冲.
- 采用电压分离电路,由连续电阻和两个二极管组成,以增强设备的特性.
- 进行了基于现实的模型的模拟,以评估在神经网络训练中改进的memristor的性能.
主要成果:
- 成功地改善了基于HfO2的memristors中的脉冲更新的线性和对称性.
- 证明了改进的设备特性导致神经网络的节能和快速训练.
- 在基于memristor交叉阵列的神经网络模拟中实现了高精度.
结论:
- 改进的memristor线性和对称性对于高精度的神经形态计算至关重要.
- 开发的方法使可训练的memristor跨条阵列基于神经网络,同时具有高能效,面积效率和精度.
- 这项工作有助于开发下一代AI硬件.
相关概念视频
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01:17Characteristics of MOSFET
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