在光子神经网络中实验实现现场反向传播
Sunil Pai1, Zhanghao Sun1, Tyler W Hughes2
1Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
概括
研究人员为机器学习开发了一种节能光子神经网络. 这种使用现场反向传播的新方法实现了与数字方法相比较高的准确性.
科学领域:
- 综合光子学
- 机器学习
- 人工智能硬件
背景情况:
- 光子神经网络 (PNN) 为机器学习提供节能,高通量计算.
- 传统的神经网络依赖于电子元件,限制了速度和效率.
- 马赫-泽恩德干扰仪 (MZI) 网格网络是PNN的关键组成部分.
研究的目的:
- 通过实验证明和验证用于训练光子神经网络的"现场反向传播".
- 评估光子神经网络在分类任务中的性能.
- 分析光子机器学习的可扩展性和能效.
主要方法:
- 一个三层,四端口光子神经网络的实验训练.
- 使用可编程相变器和光学功率监控.
- 通过干扰向前和向后传播的光来实现"现场反向传播",以测量梯度.
- 在MNIST图像识别上模拟较大的64端口网络.
主要成果:
- 光子神经网络实现了与数字模拟相比较高的测试精度 (94%).
- 成功测量相变器电压的反向传播梯度.
- 在光子硬件上训练MNIST识别等复杂任务的可行性.
- 能量的扩展分析表明了通往可扩展机器学习的途径.
结论:
- 在现场反向传播是训练光子神经网络的可行和有效方法.
- 光子神经网络在节能和高性能机器学习方面具有显著的前景.
- 开发的技术为可扩展和实用的光子计算应用铺平了道路.
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