Large-scale integrated optoelectronic chaos for machine learning acceleration

Chaos has emerged as a useful resource for machine learning, yet traditional nonlinear circuits face speed bottlenecks. Optical chaos sources offer an attractive alternative with ultra-wideband operation and massive parallelism, but existing schemes must trade single-channel throughput against multi-channel scalability. Here, we demonstrate an integrated microcomb-optoelectronic chaos engine (iMOCE). By driving an optoelectronic nonlinear cavity with a chaotic microcomb, the iMOCE generates massively parallel chaos with a 6-dB bandwidth of 25 GHz per channel, representing a two-order-of-magnitude improvement over previous microcomb-based approaches. The system delivers a total random-bit generation rate of 32.768 Tbps and accelerates four representative tasks. Compared with MCU/GPU baselines, it reduces per-inference time by about two orders of magnitude. These results establish iMOCE as a scalable, massively parallel chaos primitive for machine learning acceleration.

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Publication Details

Journal
Nature Communications
Published
2026-06-12
DOI
https://doi.org/10.1038/s41467-026-73440-2
Primary Topic
Advanced Fiber Laser Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Large-scale integrated optoelectronic chaos for machine learning acceleration

康哲, Zhouyang Pan, Shilong Pan, Jianqi Hu et al.
Nature Communications
Advanced Fiber Laser Technologies
article

Large-scale integrated optoelectronic chaos for machine learning acceleration

康哲, Zhouyang Pan, Shilong Pan, Jianqi Hu, Lihan Wang, P LI, Mengya Zong, Yue Yuan, Hao Wang, Simin Li, Jiaqi Shen, J Z Guo, Zhekai Zheng, Dan Zhu, Yuxin Liang, Jijun He, Zhihui Li, Ding Cui
article en

Abstract

Chaos has emerged as a useful resource for machine learning, yet traditional nonlinear circuits face speed bottlenecks. Optical chaos sources offer an attractive alternative with ultra-wideband operation and massive parallelism, but existing schemes must trade single-channel throughput against multi-channel scalability. Here, we demonstrate an integrated microcomb-optoelectronic chaos engine (iMOCE). By driving an optoelectronic nonlinear cavity with a chaotic microcomb, the iMOCE generates massively parallel chaos with a 6-dB bandwidth of 25 GHz per channel, representing a two-order-of-magnitude improvement over previous microcomb-based approaches. The system delivers a total random-bit generation rate of 32.768 Tbps and accelerates four representative tasks. Compared with MCU/GPU baselines, it reduces per-inference time by about two orders of magnitude. These results establish iMOCE as a scalable, massively parallel chaos primitive for machine learning acceleration.

Nature Communications
United Microelectronics (United States) (US), Nanjing Medical University (CN), University of Hong Kong (HK), Nanjing University of Aeronautics and Astronautics (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province
Openalex Percentile: Top 6%
Advanced Fiber Laser Technologies
DOIOpen Access PDFOpenAlex
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