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从溶液处理的碳纳米管中获得可扩展的突触晶体管内存,用于高速的神经形态数据处理.

Jingfang Pei1, Lekai Song1, Pengyu Liu1

  • 1Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong SAR, 999077, China.

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概括
此摘要是机器生成的。

研究人员开发了可扩展的碳纳米管突触晶体管,用于高效的神经形态计算. 这些设备能够实现高速数据处理和并行图像分析,为先进的人工智能应用铺平了道路.

关键词:
碳纳米管是如何使用的卷积内核的卷积内核是什么神经形态计算是一种神经形态计算.突触记忆是一种突触记忆.薄膜晶体管 薄膜晶体管

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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:

  • 神经网络需要大量的计算能力来进行多次积累的操作.
  • 神经形态计算通过在内存数组中执行计算提供了一个高效的替代方案.

研究的目的:

  • 使用碳纳米管开发可扩展的突触晶体管记忆.
  • 为了证明它们在神经形态计算应用中的有效性.

主要方法:

  • 从溶液分类的碳纳米管中制造突触晶体管记忆.
  • 设备性能的描述,包括开关比率,内存窗口和响应延迟.
  • 设计和实施用于并行图像处理的硬件卷积内核.

主要成果:

  • 晶体管实现了开关比>105,内存窗口约12V,响应延迟在几十纳秒.
  • 统一的设备特性,内存窗口的变化为1.8%,表明制造可扩展性.
  • 证明了以1M位/秒/频道的并行图像处理和模拟自动驾驶的高速视频处理.

结论:

  • 碳纳米管突触晶体管对于神经形态计算非常高效.
  • 开发的设备显示出对工业规模制造和高速数据处理的前景.
  • 这些发现支持神经形态计算在自动驾驶等应用中的潜力.