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门绝缘体堆工程为完全CMOS兼容的储计算.

Joon Hwang1, Min-Kyu Park2, Jeonghyun Kim1

  • 1Department of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul, 08826, South Korea.

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概括
此摘要是机器生成的。

这项研究展示了第一个使用门绝缘体堆工程的完全CMOS兼容的水库计算系统. 这种新的方法有效地处理时间数据,大大减少网络资源,精度损失最小.

关键词:
这是一个充电陷.闪存内存是一个闪存内存.整合 整合 整合神经形态计算是一种神经形态计算.储水库计算器 储水库计算尖的神经网络的神经网络.

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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:

  • 人工智能的时间数据集日益复杂,需要先进的计算架构.
  • 储计算提供了一个有前途的方法,利用短期内存将数据映射到高维空间.

研究的目的:

  • 为了展示第一个完全兼容CMOS的储计算系统.
  • 为了研究将门绝缘体堆工程用于集成水库计算和神经形态应用.
  • 分析偏差条件对水库性能的影响.

主要方法:

  • 在单片晶圆上制造CMOS电路和Al2O3/Si3N4 (A/N) 装置,用于储存器和神经元功能.
  • 整合Al2O3/Si3N4/SiO2 (A/N/O) 设备作为突触元件.
  • 在各种偏差条件下对水库性能进行实验分析.

主要成果:

  • 在单个晶圆上成功地联合集成了CMOS电路,A/N储/神经元设备和A/N/O突触设备.
  • 展示有效的时间数据处理能力.
  • 实现了大约53%的网络资源减少,只有~0.17%p的精度下降.
  • 展示了对设备变化的强度.

结论:

  • 开发的门绝缘体堆工程使得完全兼容CMOS的水库计算成为可能.
  • 同集成系统为时间数据处理提供了显著的资源减少.
  • 这种方法为高效的芯片上神经形态计算解决方案铺平了道路.