Opto-ViT: Architecting a Near-Sensor Region of Interest-Aware Vision Transformer Accelerator with Silicon Photonics
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909690303086592 |
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| author | Morsali, Mehrdad Zhou, Chengwei Najafi, Deniz Sarkar, Sreetama Mercati, Pietro Khoshavi, Navid Beerel, Peter Nikdast, Mahdi Datta, Gourav Angizi, Shaahin |
| author_facet | Morsali, Mehrdad Zhou, Chengwei Najafi, Deniz Sarkar, Sreetama Mercati, Pietro Khoshavi, Navid Beerel, Peter Nikdast, Mahdi Datta, Gourav Angizi, Shaahin |
| contents | Vision Transformers (ViTs) have emerged as a powerful architecture for computer vision tasks due to their ability to model long-range dependencies and global contextual relationships. However, their substantial compute and memory demands hinder efficient deployment in scenarios with strict energy and bandwidth limitations. In this work, we propose OptoViT, the first near-sensor, region-aware ViT accelerator leveraging silicon photonics (SiPh) for real-time and energy-efficient vision processing. Opto-ViT features a hybrid electronic-photonic architecture, where the optical core handles compute-intensive matrix multiplications using Vertical-Cavity Surface-Emitting Lasers (VCSELs) and Microring Resonators (MRs), while nonlinear functions and normalization are executed electronically. To reduce redundant computation and patch processing, we introduce a lightweight Mask Generation Network (MGNet) that identifies regions of interest in the current frame and prunes irrelevant patches before ViT encoding. We further co-optimize the ViT backbone using quantization-aware training and matrix decomposition tailored for photonic constraints. Experiments across device fabrication, circuit and architecture co-design, to classification, detection, and video tasks demonstrate that OptoViT achieves 100.4 KFPS/W with up to 84% energy savings with less than 1.6% accuracy loss, while enabling scalable and efficient ViT deployment at the edge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Opto-ViT: Architecting a Near-Sensor Region of Interest-Aware Vision Transformer Accelerator with Silicon Photonics Morsali, Mehrdad Zhou, Chengwei Najafi, Deniz Sarkar, Sreetama Mercati, Pietro Khoshavi, Navid Beerel, Peter Nikdast, Mahdi Datta, Gourav Angizi, Shaahin Hardware Architecture Vision Transformers (ViTs) have emerged as a powerful architecture for computer vision tasks due to their ability to model long-range dependencies and global contextual relationships. However, their substantial compute and memory demands hinder efficient deployment in scenarios with strict energy and bandwidth limitations. In this work, we propose OptoViT, the first near-sensor, region-aware ViT accelerator leveraging silicon photonics (SiPh) for real-time and energy-efficient vision processing. Opto-ViT features a hybrid electronic-photonic architecture, where the optical core handles compute-intensive matrix multiplications using Vertical-Cavity Surface-Emitting Lasers (VCSELs) and Microring Resonators (MRs), while nonlinear functions and normalization are executed electronically. To reduce redundant computation and patch processing, we introduce a lightweight Mask Generation Network (MGNet) that identifies regions of interest in the current frame and prunes irrelevant patches before ViT encoding. We further co-optimize the ViT backbone using quantization-aware training and matrix decomposition tailored for photonic constraints. Experiments across device fabrication, circuit and architecture co-design, to classification, detection, and video tasks demonstrate that OptoViT achieves 100.4 KFPS/W with up to 84% energy savings with less than 1.6% accuracy loss, while enabling scalable and efficient ViT deployment at the edge. |
| title | Opto-ViT: Architecting a Near-Sensor Region of Interest-Aware Vision Transformer Accelerator with Silicon Photonics |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2507.07044 |