使用多层次的Cell FeFET进行内存计算交叉杆的首次演示
Taha Soliman1, Swetaki Chatterjee2,3, Nellie Laleni4
1Robert Bosch GmbH, Renningen, Germany. taha.soliman@de.bosch.com.
Nature communications
|October 10, 2023
概括
本研究介绍了一种新的内存计算 (IMC) 交叉杆宏,使用多级铁电场效应晶体管 (FeFET) 细胞进行高效的多位倍增和积累 (MAC) 操作,实现高精度和性能.
科学领域:
- 半导体设备物理学 半导体设备物理
- 人工智能硬件是人工智能的硬件.
- 计算机架构 计算机架构
背景情况:
- 人工智能 (AI) 的进步需要高效的计算和内存解决方案.
- 内存计算 (IMC) 架构为应对这些挑战提供了一个有前途的方法.
- 铁电场效应晶体管 (FeFET) 正在成为高级内存和计算的关键组件.
研究的目的:
- 引入一个新的IMC横杆宏,采用多层FeFET单元.
- 为了使多位的乘法和积累 (MAC) 操作使用单个FeFET每电阻 (1FeFET-1R) 单元.
- 为了最大限度地减少设备变化对IMC计算准确性的影响.
主要方法:
- 使用28nmHKMG技术的FeFET设备设计和制造1FeFET-1R横杆宏.
- 在MAC操作中利用FeFET中存储数据的电气特性.
- 编码MAC导致激活时间和累积电流,绕过传统的电阻式内存方法.
主要成果:
- 实验验证显示高精度的手写识别 (96.6%) 和图像分类 (91.5%) 没有再培训.
- 实现了885.4 TOPS/W的特殊能源效率,几乎是现有设计的两倍.
- 使用多态FeFET单元格成功实现了完整的MAC操作宏,保持了交叉杆密度.
结论:
- 拟议的基于FeFET的IMC宏为高性能,低功耗AI硬件提供了可行的解决方案.
- 这项工作代表了实现密集,高效的内存计算架构的重大进步.
- 这种新的方法展示了未来人工智能加速器的多层FeFET细胞的潜力.
相关概念视频
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Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
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