Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913599904022528 |
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| author | Sinaga, Jason Clerico, Victoria Kaiser, Md Abdullah-Al Snyder, Shay Lohia, Arya Schwartz, Gregory Parsa, Maryam Jaiswal, Akhilesh |
| author_facet | Sinaga, Jason Clerico, Victoria Kaiser, Md Abdullah-Al Snyder, Shay Lohia, Arya Schwartz, Gregory Parsa, Maryam Jaiswal, Akhilesh |
| contents | Recent advances in retinal neuroscience have fueled various hardware and algorithmic efforts to develop retina-inspired solutions for computer vision tasks. In this work, we focus on a fundamental visual feature within the mammalian retina, Object Motion Sensitivity (OMS). Using DVS data from EV-IMO dataset, we analyze the performance of an algorithmic implementation of OMS circuitry for motion segmentation in presence of ego-motion. This holistic analysis considers the underlying constraints arising from the hardware circuit implementation. We present novel CMOS circuits that implement OMS functionality inside image sensors, while providing run-time re-configurability for key algorithmic parameters. In-sensor technologies for dynamical environment adaptation are crucial for ensuring high system performance. Finally, we verify the functionality and re-configurability of the proposed CMOS circuit designs through Cadence simulations in 180nm technology. In summary, the presented work lays foundation for hardware-algorithm re-engineering of known biological circuits to suit application needs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08320 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation Sinaga, Jason Clerico, Victoria Kaiser, Md Abdullah-Al Snyder, Shay Lohia, Arya Schwartz, Gregory Parsa, Maryam Jaiswal, Akhilesh Neural and Evolutionary Computing Image and Video Processing Recent advances in retinal neuroscience have fueled various hardware and algorithmic efforts to develop retina-inspired solutions for computer vision tasks. In this work, we focus on a fundamental visual feature within the mammalian retina, Object Motion Sensitivity (OMS). Using DVS data from EV-IMO dataset, we analyze the performance of an algorithmic implementation of OMS circuitry for motion segmentation in presence of ego-motion. This holistic analysis considers the underlying constraints arising from the hardware circuit implementation. We present novel CMOS circuits that implement OMS functionality inside image sensors, while providing run-time re-configurability for key algorithmic parameters. In-sensor technologies for dynamical environment adaptation are crucial for ensuring high system performance. Finally, we verify the functionality and re-configurability of the proposed CMOS circuit designs through Cadence simulations in 180nm technology. In summary, the presented work lays foundation for hardware-algorithm re-engineering of known biological circuits to suit application needs. |
| title | Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation |
| topic | Neural and Evolutionary Computing Image and Video Processing |
| url | https://arxiv.org/abs/2408.08320 |