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13 March 2024 Reconfigurable time-space photonic integrated convolutional accelerator
Tigers Jonuzi, Alessandro Lupo, Lucas Talandier, Mirko Goldmann, Ingo Fischer, Apostolos Argyris, Serge Massar, Miguel C. Soriano, J. David Domenech Gomez
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Proceedings Volume 12903, AI and Optical Data Sciences V; 1290304 (2024) https://doi.org/10.1117/12.3002622
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Convolutional Neural Networks (CNNs) are employed in a plethora of fields, including computer vision, natural language processing, and speech recognition. We present an integrated photonic accelerator for CNNs based on the temporal-spatial interleaving of signals. This architecture supports 1D kernels, and can be extended to 2D convolutional kernels, providing scalability for complex networks. A supervised on-chip learning algorithm is employed to guarantee a reliable setting of convolutional weights against fabrication tolerances, thermal cross-talks, and changes in operating conditions. Overall, by leveraging photonics technology, the proposed accelerator significantly reduces hardware complexity while enabling high-speed processing and parallelism.
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Tigers Jonuzi, Alessandro Lupo, Lucas Talandier, Mirko Goldmann, Ingo Fischer, Apostolos Argyris, Serge Massar, Miguel C. Soriano, and J. David Domenech Gomez "Reconfigurable time-space photonic integrated convolutional accelerator", Proc. SPIE 12903, AI and Optical Data Sciences V, 1290304 (13 March 2024); https://doi.org/10.1117/12.3002622
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KEYWORDS
Photonic integrated circuits
Integrated optics
Design
Convolutional neural networks
Optical modulators
Signal processing
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