All-analog photoelectronic chip for high-speed vision tasks

Yitong Chen, Maimaiti Nazhamaiti, Han Xu, Yao Meng, Tiankuang Zhou, Guangpu Li, Jingtao Fan, Qi Wei, Jiamin Wu (), Fei Qiao (), Lu Fang () and Qionghai Dai ()
Additional contact information
Yitong Chen: Tsinghua University
Maimaiti Nazhamaiti: Tsinghua University
Han Xu: Tsinghua University
Yao Meng: Tsinghua University
Tiankuang Zhou: Tsinghua University
Guangpu Li: Tsinghua University
Jingtao Fan: Tsinghua University
Qi Wei: Tsinghua University
Jiamin Wu: Tsinghua University
Fei Qiao: Tsinghua University
Lu Fang: Tsinghua University
Qionghai Dai: Tsinghua University

Nature, 2023, vol. 623, issue 7985, 48-57

Abstract: Abstract Photonic computing enables faster and more energy-efficient processing of vision data1–5. However, experimental superiority of deployable systems remains a challenge because of complicated optical nonlinearities, considerable power consumption of analog-to-digital converters (ADCs) for downstream digital processing and vulnerability to noises and system errors1,6–8. Here we propose an all-analog chip combining electronic and light computing (ACCEL). It has a systemic energy efficiency of 74.8 peta-operations per second per watt and a computing speed of 4.6 peta-operations per second (more than 99% implemented by optics), corresponding to more than three and one order of magnitude higher than state-of-the-art computing processors, respectively. After applying diffractive optical computing as an optical encoder for feature extraction, the light-induced photocurrents are directly used for further calculation in an integrated analog computing chip without the requirement of analog-to-digital converters, leading to a low computing latency of 72 ns for each frame. With joint optimizations of optoelectronic computing and adaptive training, ACCEL achieves competitive classification accuracies of 85.5%, 82.0% and 92.6%, respectively, for Fashion-MNIST, 3-class ImageNet classification and time-lapse video recognition task experimentally, while showing superior system robustness in low-light conditions (0.14 fJ μm−2 each frame). ACCEL can be used across a broad range of applications such as wearable devices, autonomous driving and industrial inspections.

Date: 2023
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DOI: 10.1038/s41586-023-06558-8

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