基于阵列波导网格的光学神经网络的无冗余集成光学卷轴器
Shiji Zhang1, Haojun Zhou1, Bo Wu1
1Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Nanophotonics (Berlin, Germany)
|December 5, 2024
概括
这项研究介绍了一种用于人工智能的新型光学卷积架构. 它显著减少了冗余,并提高了光学神经网络 (ONN) 的效率,以实现更快的AI计算.
科学领域:
- 光电学是指光电子产品.
- 人工智能的人工智能
- 计算机工程 计算机工程
背景情况:
- 光学神经网络 (ONN) 为人工智能提供高速和节能计算.
- 光学卷积至关重要,但目前的架构遭受了输入冗余和资源浪费.
研究的目的:
- 开发一个集成的光学卷积架构,消除冗余.
- 为了利用阵列波导网格 (AWG) 进行高效的卷积操作.
主要方法:
- 使用AWG原则设计了一个集成的光学卷积架构.
- 实现了一个执行M x N乘积-积累运算的系统,每周期使用M + N单位.
- 通过手写数字识别实验验验证了架构.
主要成果:
- 在手写数字识别中实现了5位精度和91.9%的准确性.
- 展示了一个没有冗余的架构,低功耗.
- 报告的高计算密度为8.53 teraOP 毫米-1 秒-1.1.
结论:
- 开发的基于AWG的光学卷积架构显著减少了冗余并提高了效率.
- 该方法为光学神经网络提供了可扩展,低功耗和高密度的解决方案.
- 这项工作通过优化的光学硬件来推进高性能计算和AI应用.
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