完全集成的光子张力芯用于图像卷积
Bicky A Marquez1,2, Jagmeet Singh1, Hugh Morison1
1Department of Physics, Engineering Physics and Astronomy, Queen's University, Kingston, ON KL7 3N6, Canada.
Nanotechnology
|June 15, 2023
概括
光子加速图像处理,使用集成光学元件进行矩阵乘法. 这项技术通过加快关键操作来增强卷积神经网络,为更高效的AI硬件提供了途径.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 卷积在信号和图像处理中是至关重要的,它在很大程度上依赖于点积和矩阵乘法.
- 先进的图像处理和卷积神经网络 (CNN) 需要大量的计算能力来完成这些操作.
- 光子学为加速并行矩阵乘法提供了一个有希望的途径.
研究的目的:
- 实验证明一种光子学方法,用于执行对图像卷积至关重要的矩阵乘法.
- 开发一个散射矩阵模型来模拟和预测大规模光子系统的性能.
主要方法:
- 采用多波长方法,集成调节器和微环共振器调节过器作为重量库.
- 在图像卷积中使用平衡探测器进行高效的矩阵乘法.
- 根据实验数据开发和验证一个散射矩阵模型.
主要成果:
- 通过使用集成光子学成功地实验证明了用于图像卷积的矩阵乘法.
- 为大型光子系统开发预测散射矩阵模型.
- 性能限制的分析,包括通道间交叉和比特分辨率.
结论:
- 集成的光子学可以有效地加速对AI和信号处理至关重要的图像卷积操作.
- 开发的散射矩阵模型为设计和优化未来大型光子计算系统提供了一个工具.
- 这项工作突出了光子硬件在克服现代深度学习架构中的计算瓶方面的潜力.
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