基于RRAM的模拟内存计算和传感器的单体3D集成,用于节能近传感器计算
Yiwei Du1, Jianshi Tang1, Yijun Li1
1School of Integrated Circuits, Beijing Advanced Innovation Center for Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China.
Advanced materials (Deerfield Beach, Fla.)
|August 31, 2023
概括
这项研究引入了一种新的近传感器计算 (NSC) 架构,M3D-SAIL,集成光传感器和模拟内存计算 (CIM). 这一创新显著提高了数据密集型物联网应用程序的能源效率和处理速度.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 物联网 (IoT) 产生了大量的数据,压迫了带宽和能源效率.
- 当前的计算架构在源头处理数据时面临限制.
- 近传感器计算 (NSC) 提供了一个解决方案,通过处理更接近数据生成的地方的数据.
研究的目的:
- 展示一个单体的三维 (M3D) 集成NSC架构 (M3D-SAIL).
- 结合光传感器阵列,模拟内存计算 (CIM) 和Si互补金属氧化物半导体 (CMOS) 逻辑电路.
- 为了实现传感器数据的高能效和处理速度.
主要方法:
- 开发了基于Si CMOS逻辑的M3D集成,InGaZnOx场效应晶体管 (IGZO-FET) 和基于CIM的电阻随机存储器 (RRAM),以及IGZO-FET光传感器阵列.
- 使用1k比特1T1R阵列进行模拟CIM.
- 验证了每个集成层的结构完整性和功能.
主要成果:
- 使用M3D-SAIL架构成功实现了NSC.
- 实现了96.7%的分类准确度,用于视频关键提取.
- 与2D对应器相比,显示了能源消耗减少了31.5倍,计算速度增加了1.91倍.
结论:
- 采用M3D-SAIL架构,可以实现高效的NSC.
- 这种方法解决了物联网数据处理中的关键带宽和能源挑战.
- M3D集成为先进的现场传感器数据分析提供了一个有前途的途径.
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