An on-chip photonic deep neural network for image classification
An on-chip photonic deep neural network for image classification
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DOI：
10.1038/s41586-022-04714-0
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发表时间：
2022-06-01
期刊：
影响因子：
64.8
通讯作者：
Aflatouni, Firooz
中科院分区：
文献类型：
--
作者：
Ashtiani, Farshid;Geers, Alexander J.;Aflatouni, Firooz
从计算机视觉到医学诊断（1-5）等应用的深度神经网络通常使用基于时钟的处理器（6-14）来实现，其中计算速度主要受到时钟频率和内存访问时间的限制。在光学领域，尽管光子计算取得了进步(15-17)，但缺乏可扩展的片上光学非线性以及光子器件的损耗限制了光学深层网络的可扩展性。在这里，我们报告了一种集成的端到端光子深度神经网络（PDNN），该网络通过直接处理在神经元层中传播时撞击片上像素阵列的光波来执行亚纳秒图像分类。在每个神经元中，通过光学方式执行线性计算，并通过光电方式实现非线性激活函数，从而使分类时间低于 570 ps，与最先进的数字平台的单个时钟周期相当。均匀分布的光源提供相同的每神经元光学输出范围，从而可扩展至大规模 PDNN。展示了手写字母的二类和四类分类，准确率分别高于 93.8% 和 89.8%。直接、无时钟的光学数据处理消除了模数转换和对大型内存模块的需求，从而为下一代深度学习系统提供了更快、更节能的神经网络。
Deep neural networks with applications from computer vision to medical diagnosis(1-5) are commonly implemented using clock-based processors(6-14), in which computation speed is mainly limited by the clock frequency and the memory access time. In the optical domain, despite advances in photonic computation(15-17), the lack of scalable on-chip optical non-linearity and the loss of photonic devices limit the scalability of optical deep networks. Here we report an integrated end-to-end photonic deep neural network (PDNN) that performs sub-nanosecond image classification through direct processing of the optical waves impinging on the on-chip pixel array as they propagate through layers of neurons. In each neuron, linear computation is performed optically and the non-linear activation function is realized opto-electronically, allowing a classification time of under 570 ps, which is comparable with a single clock cycle of state-of-the-art digital platforms. A uniformly distributed supply light provides the same per-neuron optical output range, allowing scalability to large-scale PDNNs. Two-class and four-class classification of handwritten letters with accuracies higher than 93.8% and 89.8%, respectively, is demonstrated. Direct, clock-less processing of optical data eliminates analogue-to-digital conversion and the requirement for a large memory module, allowing faster and more energy efficient neural networks for the next generations of deep learning systems.