Delocalized photonic deep learning on the internet's edge
Delocalized photonic deep learning on the internet's edge
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DOI：
10.1126/science.abq8271
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发表时间：
2022-10-20
期刊：
影响因子：
56.9
通讯作者：
Englund, Dirk
中科院分区：
文献类型：
--
作者：
Sludds, Alexander;Bandyopadhyay, Saumil;Englund, Dirk
由于功率、处理和内存的限制，先进的机器学习模型目前无法在智能传感器和无人机等边缘设备上运行。我们介绍了一种基于跨网络的离域模拟处理的机器学习推理方法。在这种名为 Netcast 的方法中，基于云的“智能收发器”将重量数据传输到边缘设备，从而实现超高效的光子推理。我们演示了每乘以 40 阿托焦耳（100 瓦）云计算机的超低光能下的图像识别。
Advanced machine learning models are currently impossible to run on edge devices such as smart sensors and unmanned aerial vehicles owing to constraints on power, processing, and memory. We introduce an approach to machine learning inference based on delocalized analog processing across networks. In this approach, named Netcast, cloud-based "smart transceivers" stream weight data to edge devices, enabling ultraefficient photonic inference. We demonstrate image recognition at ultralow optical energy of 40 attojoules per multiply (100 watts) cloud computers.