An on-chip photonic deep neural network for image classification
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 co...
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Published in | Nature (London) Vol. 606; no. 7914; pp. 501 - 506 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
16.06.2022
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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Summary: | 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.
Using a three-layer opto-electronic neural network, direct, clock-less sub-nanosecond image classification on a silicon photonics chip is demonstrated, achieving a classification time comparable with a single clock cycle of state-of-the-art digital implementations. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0028-0836 1476-4687 |
DOI: | 10.1038/s41586-022-04714-0 |