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在芯片上的光子深度神经网络用于图像分类
Farshid Ashtiani1, Alexander J Geers1, Firooz Aflatouni2
1Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
Nature
|June 1, 2022
概括
研究人员开发了一个集成的光子深度神经网络 (PDNN) 用于亚纳秒图像分类. 这种新的方法绕过了传统的电子限制,使得更快,更节能的深度学习系统成为可能.
科学领域:
- 光子学
- 深度学习
- 计算机视觉
背景情况:
- 深度神经网络 (DNN) 对于医疗诊断等任务至关重要,但由于电子处理器的时钟速度和内存访问而受到限制.
- 现有的光子计算在芯片上的光学非线性和设备损失方面面临挑战,这阻碍了光学DNN的可扩展性.
研究的目的:
- 报告一个能够分类亚纳秒图像的集成端到端光子深度神经网络 (PDNN).
- 在光子系统中展示一种新的光电子计算方法.
主要方法:
- 通过直接处理光波进行图像分类的集成PDNN.
- 在每个神经元内实现光学线性计算和光电子非线性激活.
- 使用均分布的供应光来实现可扩展的每神经元光学输出.
主要成果:
- 在570比秒内实现图像分类,相当于数字平台的单个时钟周期.
- 在手写字母的分类中,高准确度:>93.8%的二类和>89.8%的四类.
- 展示了PDNN对于大规模应用的可扩展性.
结论:
- 开发的PDNN为电子深度学习系统提供了更快,更节能的替代方案.
- 消除对模拟数字转换和大型内存模块的需求加速了处理.
- 这种无时钟的光学数据处理为下一代深度学习系统铺平了道路.
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