par Jonuzi, Tigers;Masaad, Sarah;Lupo, Alessandro;David Domenech Gomez, J.;Bienstman, Peter;Massar, Serge 
Référence 2023 International Conference on Photonics in Switching and Computing (PSC)(26-29 September 2023: Mantova, Italie), 2023 International Conference on Photonics in Switching and Computing (PSC), IEEE
Publication Publié, 2023-11-02
Référence 2023 International Conference on Photonics in Switching and Computing (PSC)(26-29 September 2023: Mantova, Italie), 2023 International Conference on Photonics in Switching and Computing (PSC), IEEE
Publication Publié, 2023-11-02
Publication dans des actes
| Résumé : | Convolutional Neural Networks (CNNs) are fundamental machine learning tools to process image, speech, or audio signal inputs. The convolutional layer is the core building block of a CNN, and it is where most of the computation occurs. Here, we propose an integrated photonic convolutional accelerator based on time-spatial interleaving utilizing standard generic building blocks to reduce hardware complexity. The architecture is suitable for addressing both 2D and 1D convolutional kernels enabling scalability to more complex networks. Furthermore, a numerical simulation demonstrates the viability of a supervised online learning algorithm for loading the kernel weights both in amplitude and in phase taking in consideration fabrication tolerances and thermal cross-talk. |
