Microcomb-enabled parallel self- calibration optical convolution streaming processor
Yi-Xiang Wang, Xingyuan Xu, Xiaotian Zhu, Yifu Xu, Shifan Chen, Haoran Zhang, Yue Zheng, Shuying Li, Yunping Bai, Zhihui Liu, Roberto Morandotti, Brent E. Little, Sai Tak Chu, Arthur James Lowery, David J. Moss, Kun Xu · Light Science & Applications · 2026
The exponential growth of cloud computing and artificial intelligence (AI) applications has driven an urgent need for high-bandwidth, energy-efficient hardware architectures in data centers. With Moore's Law nearing its limits, optical neuromorphic computing hardware offers a promising alternative, providing ultra-high speeds and minimal energy consumption due to its analog architecture. Here, we propose the microcomb-enabled parallel optical convolution streaming processor (OCSP) with time, space, and wavelength three-dimensional multiplexing, operating at data rates of 50 GBaud or higher, achieving a convolution computing speed of up to 4 trillion operations per second (TOPS). Moreover, the OCSP uses a robust self-calibration mechanism to achieve accurate optical phase calibration and set-up of its convolution function. This innovative approach leverages time-space interleaving passive periodic interference architecture, incorporating wavelength-division-multiplexing technology, and is verified experimentally for parallel image feature extraction and recognition tasks. Our OCSP offers a practical pathway for seamlessly integrating photonic computing units into data center interconnects, unlocking photonic computing's potential for scalable, low-latency AI workloads.