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超低功率内传感器神经计算与振荡性视网膜神经元用于频率复杂,并行机器视觉.

Ragib Ahsan1, Hyun Uk Chae1, Seyedeh Atiyeh Abbasi Jalal1

  • 1Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.

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概括
此摘要是机器生成的。

这项研究引入了使用合振荡神经元设备进行高效图像处理的新型传感器内计算. 这种方法在边缘检测和数字识别等任务中比传统方法取得了显著的功率和速度优势.

关键词:
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科学领域:

  • 神经形态工程的神经形态工程
  • 综合光子学 综合光子学
  • 人工智能 硬件 硬件

背景情况:

  • 传统的计算架构 (·诺伊曼) 由于数据移动和转换而面临功率和速度的限制.
  • 当前的传感器内计算通常依赖于可调节的传感器或对线性操作的权重元件.
  • 在传感器层面高效,低功耗的处理对于先进的人工智能和边缘计算应用至关重要.

研究的目的:

  • 实现传感器内计算,使用振荡神经元设备进行并行,非线性运算.
  • 在焦平面阵列上直接演示图像处理功能和神经网络推断.
  • 为了预测这种新的传感器内计算方法的能源效率.

主要方法:

  • 利用合振荡视网膜神经元设备将光学信号转换为电压振荡.
  • 开发了一个基于合振荡器的频率转移的计算方案,用于并行,频率复合的非线性运算.
  • 实验性地实现了一个3x3焦平面阵列用于图像处理任务和MNIST数字识别.

主要成果:

  • 在3x3阵列上展示了边缘检测,值和细分函数的并行执行.
  • 在使用神经元阵列的MNIST数据库中,成功地对手写数字进行了实验推断.
  • 预计图像处理操作的超低能耗,可能低至15 aJ/OP.

结论:

  • 传感器内计算与合的振荡神经元能够在传感器上直接进行高效,并行,非线性计算.
  • 这种方法在图像处理和AI推断方面在功率和速度方面提供了显著的优势.
  • 该技术显示出下一代低功耗,高性能边缘计算系统的前景.