TOPS‐Speed Reconfigurable Photonic Transposed Convolution Accelerator for Generative Tasks

ABSTRACT Transposed convolution, crucial in large‐parameter generative models ranging from content creation to autonomous driving, imposes substantial demands on GPU memory and energy consumption in electronic processors. Electronic processors, fundamentally limited by the von Neumann architecture and further hindered by silicon‐based quantum tunneling effects, struggle to meet the stringent real‐time requirements of modern generative workloads. In contrast, optical computing—exploiting ultra‐wide bandwidth and ultra‐low power consumption—offers a promising alternative for high‐speed transposed convolution in next‐generation AI. Here, we introduce a high‐speed and reconfigurable photonic transposed convolution accelerator (PTCA). By interleaving wavelength, temporal, and spatial dimensions and leveraging an integrated Kerr microcomb for data‐dimension expansion, the PTCA achieves tera operations per second (TOPS) with 100% bit efficiency. Experiments demonstrate a processing speed of 1.026 TOPS, making it, to the best of our knowledge, the fastest reconfigurable PTCA to date. In Fashion‐MNIST reconstruction tasks, this system achieves a mean squared error (MSE) of 0.0062 without any additional post‐processing by electronic fully connected layers. Our work thus establishes a high‐speed, reconfigurable photonic paradigm for accelerating future generative AI.

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Publication Details

Journal
Laser & Photonics Review
Published
2025-11-02
DOI
https://doi.org/10.1002/lpor.202500771
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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TOPS‐Speed Reconfigurable Photonic Transposed Convolution Accelerator for Generative Tasks

Roberto Morandotti, Xingyuan Xu, Chan Xia, Yunping Bai et al.
Laser & Photonics Review
Neural Networks and Reservoir Computing
article

TOPS‐Speed Reconfigurable Photonic Transposed Convolution Accelerator for Generative Tasks

Roberto Morandotti, Xingyuan Xu, Chan Xia, Yunping Bai, Kun Xu, Yixuan Zheng, Sai T. Chu, Sha Zhu, Xiaotian Zhu, Shifan Chen, Brent E. Little, David Moss, Yuyang Liu, Yifu Xu, Zhihui Liu
article en

Abstract

ABSTRACT Transposed convolution, crucial in large‐parameter generative models ranging from content creation to autonomous driving, imposes substantial demands on GPU memory and energy consumption in electronic processors. Electronic processors, fundamentally limited by the von Neumann architecture and further hindered by silicon‐based quantum tunneling effects, struggle to meet the stringent real‐time requirements of modern generative workloads. In contrast, optical computing—exploiting ultra‐wide bandwidth and ultra‐low power consumption—offers a promising alternative for high‐speed transposed convolution in next‐generation AI. Here, we introduce a high‐speed and reconfigurable photonic transposed convolution accelerator (PTCA). By interleaving wavelength, temporal, and spatial dimensions and leveraging an integrated Kerr microcomb for data‐dimension expansion, the PTCA achieves tera operations per second (TOPS) with 100% bit efficiency. Experiments demonstrate a processing speed of 1.026 TOPS, making it, to the best of our knowledge, the fastest reconfigurable PTCA to date. In Fashion‐MNIST reconstruction tasks, this system achieves a mean squared error (MSE) of 0.0062 without any additional post‐processing by electronic fully connected layers. Our work thus establishes a high‐speed, reconfigurable photonic paradigm for accelerating future generative AI.

Laser & Photonics ReviewVol. 20(5)
Beijing University of Posts and Telecommunications (CN), City University of Hong Kong (HK), Nankai University (CN), Institut National de la Recherche Scientifique (CA), Xi'an University of Technology (CN), China Telecom (China) (CN), China Telecom (CN), Swinburne University of Technology (AU)
National Natural Science Foundation of China, Beijing University of Posts and Telecommunications, State Key Laboratory of Information Photonics and Optical Communications, National Key Research and Development Program of China
Openalex Percentile: Top 83%
Neural Networks and Reservoir Computing
DOIOpen Access PDFOpenAlex
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