Toward High Performance, Programmable Extreme-Edge Intelligence for Neuromorphic Vision Sensors utilizing Magnetic Domain Wall Motion-based MTJ
<|reference_start|>Toward High Performance, Programmable Extreme-Edge Intelligence for Neuromorphic Vision Sensors utilizing Magnetic Domain Wall Motion-based MTJ: The desire to empower resource-limited edge devices with computer vision (CV) must overcome the high energy consumption of collecting and processing vast sensory data. To address the challenge, this work proposes an energy-efficient non-von-Neumann in-pixel processing solution for neuromorphic vision sensors employing emerging (X) magnetic domain wall magnetic tunnel junction (MDWMTJ) for the first time, in conjunction with CMOS-based neuromorphic pixels. Our hybrid CMOS+X approach performs in-situ massively parallel asynchronous analog convolution, exhibiting low power consumption and high accuracy across various CV applications by leveraging the non-volatility and programmability of the MDWMTJ. Moreover, our developed device-circuit-algorithm co-design framework captures device constraints (low tunnel-magnetoresistance, low dynamic range) and circuit constraints (non-linearity, process variation, area consideration) based on monte-carlo simulations and device parameters utilizing GF22nm FD-SOI technology. Our experimental results suggest we can achieve an average of 45.3% reduction in backend-processor energy, maintaining similar front-end energy compared to the state-of-the-art and high accuracy of 79.17% and 95.99% on the DVS-CIFAR10 and IBM DVS128-Gesture datasets, respectively.<|reference_end|>
@article{kaiser2024toward,
    title={Toward High Performance, Programmable Extreme-Edge Intelligence for
  Neuromorphic Vision Sensors utilizing Magnetic Domain Wall Motion-based MTJ},
    author={Md Abdullah-Al Kaiser, Gourav Datta, Peter A. Beerel, and Akhilesh R.
  Jaiswal},
    journal={arXiv preprint arXiv:2402.15121},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15121},
    primaryClass={cs.AR cs.ET eess.IV}
}