Photonic Neuromorphic Accelerator for Convolutional Neural Networks based on an Integrated Reconfigurable Mesh
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917662751195136 |
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| author | Tsirigotis, Aris Sarantoglou, Gerge Deligiannidis, Stavros Sanchez, Erica Gutierrez, Ana Bogris, Adonis Capmany, Jose Mesaritakis, Charis |
| author_facet | Tsirigotis, Aris Sarantoglou, Gerge Deligiannidis, Stavros Sanchez, Erica Gutierrez, Ana Bogris, Adonis Capmany, Jose Mesaritakis, Charis |
| contents | In this work, we present and experimentally validate a passive photonic-integrated neuromorphic accelerator that uses a hardware-friendly optical spectrum slicing technique through a reconfigurable silicon photonic mesh. The proposed scheme acts as an analogue convolutional engine, enabling information preprocessing in the optical domain, dimensionality reduction and extraction of spatio-temporal features. Numerical results demonstrate that utilizing only 7 passive photonic nodes, critical modules of a digital convolutional neural network can be replaced. As a result, a 98.6% accuracy on the MNIST dataset was achieved, with a power consumption reduction of at least 26% compared to digital CNNs. Experimental results confirm these findings, achieving 97.7% accuracy with only 3 passive nodes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06434 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Photonic Neuromorphic Accelerator for Convolutional Neural Networks based on an Integrated Reconfigurable Mesh Tsirigotis, Aris Sarantoglou, Gerge Deligiannidis, Stavros Sanchez, Erica Gutierrez, Ana Bogris, Adonis Capmany, Jose Mesaritakis, Charis Optics Image and Video Processing In this work, we present and experimentally validate a passive photonic-integrated neuromorphic accelerator that uses a hardware-friendly optical spectrum slicing technique through a reconfigurable silicon photonic mesh. The proposed scheme acts as an analogue convolutional engine, enabling information preprocessing in the optical domain, dimensionality reduction and extraction of spatio-temporal features. Numerical results demonstrate that utilizing only 7 passive photonic nodes, critical modules of a digital convolutional neural network can be replaced. As a result, a 98.6% accuracy on the MNIST dataset was achieved, with a power consumption reduction of at least 26% compared to digital CNNs. Experimental results confirm these findings, achieving 97.7% accuracy with only 3 passive nodes. |
| title | Photonic Neuromorphic Accelerator for Convolutional Neural Networks based on an Integrated Reconfigurable Mesh |
| topic | Optics Image and Video Processing |
| url | https://arxiv.org/abs/2405.06434 |