基于铁电道交叉点记忆器的多次累积运行和卷积神经网络的实施
Ziming Cheng1, He Wang1, Zeyu Guan1
1Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics, and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei 230026, China.
ACS applied materials & interfaces
|March 31, 2025
概括
铁电道连接 (FTJ) 记忆器可以实现高效的内存计算. 这些设备在手写数字识别等任务中实现了高精度,能源消耗明显低于传统硬件.
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
- 固态物理 固态物理
背景情况:
- 传统的·诺伊曼架构由于数据传输瓶而面临能源和速度限制.
- 内存计算通过在内存存储中执行计算提供了一个范式转变.
- 铁电道连接 (FTJ) 记忆器正在成为内存计算的有希望的候选者.
研究的目的:
- 为内存计算应用制造和描述基于Hf0.5Zr0.5O2的FTJ记忆器.
- 证明使用FTJ数组进行多重积累操作和图像处理的可行性.
- 评估使用FTJ设备用于手写数字识别的卷积神经网络的性能.
主要方法:
- 在基板上制造基于Hf0.5Zr0.5O2的FTJ.
- FTJ 设备的特性,包括导电量状态,周期变化和线性.
- 在自定义的可编程字段网关阵列 (FPGA) 板上实现FTJ阵列,用于计算任务.
- 使用FTJ设备作为卷积层测试卷积神经网络 (CNN).
主要成果:
- 演示FTJ具有32个导电量状态 (5位) 和低循环到循环变化 (1.6%).
- 在FTJ设备中实现了高度线性导电量操纵 (非线性<1).
- 使用FTJ阵列成功执行多次积累操作和图像处理.
- 使用基于FTJ的CNN实现了92.5%的手写数字识别准确度,显著提高了能源效率.
结论:
- 基于Hf0.5Zr0.5O2的FTJ显示了高效内存计算的巨大潜力.
- FTJ记忆器提供了一个可行的硬件解决方案,以克服传统计算架构的局限性.
- 这项工作为节能,高性能计算系统铺平了道路.
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