• Citer

Reconfigurable time-space photonic integrated convolutional accelerator

par Jonuzi, Tigers;Lupo, Alessandro ;Talandier, Lucas;Goldmann, Mirko;Fischer, Ingo;Argyris, Apostolos;Massar, Serge ;Soriano, Miguel Cornelles;David Domenech Gomez, J.
Référence Proceedings Volume 12903, AI and Optical Data Sciences V, Vol. 12903, page (1290304)
Publication Publié, 2024-03-13

Publication dans des actes

• Accès en ligne
• Détails
• Contenu
• Statistiques

Résumé :

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.