Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation
<|reference_start|>Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation: 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.<|reference_end|>
@article{sinaga2024hardware-algorithm,
    title={Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent
  Object Motion Segmentation},
    author={Jason Sinaga (1), Victoria Clerico (2,3), Md Abdullah-Al Kaiser (1),
  Shay Snyder (2), Arya Lohia (2), Gregory Schwartz (4), Maryam Parsa (2),
  Akhilesh Jaiswal (1) (University of Wisconsin - Madison (1), George Mason
  University (2), Universidad Polit'ecnica de Madrid (3), Northwestern
  University (4))},
    journal={arXiv preprint arXiv:2408.08320},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08320},
    primaryClass={cs.NE eess.IV}
}