Large-scale integrated optoelectronic chaos for machine learning acceleration
- Zhouyang Pan
- Zhekai Zheng
- Ping Li
- Hao Wang
- Jiacheng Guo
- Ding Cui
- Zhihui Li
- Jiaqi Shen
- Lihan Wang
- Mengya Zong
- Simin Li
- Zhe Kang
- Yue Yuan
- Jianqi Hu
- Jijun He
- Yuxin Liang
- Dan Zhu
- Shilong Pan
2026-06-12
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.