There is an ever-growing demand for artificial intelligence. Optical processors, which compute with photons instead of electrons, can fundamentally accelerate the development of artificial intelligence by offering substantially improved computing performance. There has been long-term interest in optically constructing the most widely used artificial-intelligence architecture, that is, artificial neural networks, to achieve brain-inspired information processing at the speed of light. However, owing to restrictions in design flexibility and the accumulation of system errors, existing processor architectures are not reconfigurable and have limited model complexity and experimental performance. Here, we propose the reconfigurable diffractive processing unit, an optoelectronic fused computing architecture based on the diffraction of light, which can support different neural networks and achieve a high model complexity with millions of neurons. Along with the developed adaptive training approach to circumvent system errors, we achieved excellent experimental accuracies for high-speed image and video recognition over benchmark datasets and a computing performance superior to that of cutting-edge electronic computing platforms.
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We thank T. Zhu and T. Yan for assistance with the software for the prototype system. This work is supported by the Beijing Municipal Science and Technology Commission (No. Z181100003118014), the National Key Research and Development Program of China (No. 2020AAA0130000), the National Natural Science Foundation of China (No. 62088102 and No. 61860206003) and the Tsinghua University Initiative Scientific Research Program.
The authors declare no competing interests.
Peer review information Nature Photonics thanks Nathan Youngblood and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Supplementary Figs. 1–10, discussion and Tables 1–4.
Experimental demonstration by using a camera to capture the handwritten digits as the inputs for the system.
Experimental results of D2NN on the MNIST database.
Experimental results of D-NIN-1 on the MNIST database.
Experimental results of D-RNN on the Weizmann database.
Experimental results of D-RNN on the KTH database.
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Zhou, T., Lin, X., Wu, J. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics 15, 367–373 (2021). https://doi.org/10.1038/s41566-021-00796-w
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