Microcomb-based integrated photonic processing unit

Bowen Bai, Qipeng Yang, Haowen Shu, Lin Chang (), Fenghe Yang, Bitao Shen, Zihan Tao, Jing Wang, Shaofu Xu, Weiqiang Xie, Weiwen Zou, Weiwei Hu, John E. Bowers () and Xingjun Wang ()
Additional contact information
Bowen Bai: Peking University
Qipeng Yang: Peking University
Haowen Shu: Peking University
Lin Chang: Peking University
Fenghe Yang: Zhangjiang Laboratory
Bitao Shen: Peking University
Zihan Tao: Peking University
Jing Wang: Shanghai Jiao Tong University
Shaofu Xu: Shanghai Jiao Tong University
Weiqiang Xie: University of California
Weiwen Zou: Shanghai Jiao Tong University
Weiwei Hu: Peking University
John E. Bowers: University of California
Xingjun Wang: Peking University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract The emergence of parallel convolution-operation technology has substantially powered the complexity and functionality of optical neural networks (ONN) by harnessing the dimension of optical wavelength. However, this advanced architecture faces remarkable challenges in high-level integration and on-chip operation. In this work, convolution based on time-wavelength plane stretching approach is implemented on a microcomb-driven chip-based photonic processing unit (PPU). To support the operation of this processing unit, we develop a dedicated control and operation protocol, leading to a record high weight precision of 9 bits. Moreover, the compact architecture and high data loading speed enable a preeminent photonic-core compute density of over 1 trillion of operations per second per square millimeter (TOPS mm−2). Two proof-of-concept experiments are demonstrated, including image edge detection and handwritten digit recognition, showing comparable processing capability compared to that of a digital computer. Due to the advanced performance and the great scalability, this parallel photonic processing unit can potentially revolutionize sophisticated artificial intelligence tasks including autonomous driving, video action recognition and image reconstruction.

Date: 2023
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DOI: 10.1038/s41467-022-35506-9

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