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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.00744 |
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| _version_ | 1866917550355382272 |
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| author | Singh, Kushagra Das, Debasis |
| author_facet | Singh, Kushagra Das, Debasis |
| contents | Traditional edge detection algorithms, foundational to computer vision, face significant challenges in energy efficiency and processing latency on conventional CMOS-based hardware. Existing algorithms, such as Canny, are computationally expensive, posing challenges in resource-constrained hardware where energy efficiency and low latency are critical. This study introduces a novel, hardware-efficient algorithm that leverages the intrinsic characteristics of magnetic tunnel junction (MTJ) devices. We present a detailed device-level analysis of an MTJ-based system for edge detection, outlining its operational cycles, including write, read, and reset methods. The algorithm's efficacy is evaluated against the standard Canny edge detection method. We provide a quantitative performance analysis, including metrics such as energy consumption and latency, which demonstrates that our proposed spintronics-based approach offers a promising solution for achieving low-power, high-speed image processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00744 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Edge Detection Framework Utilizing SOT-MTJ Bit-Cell Arrays Singh, Kushagra Das, Debasis Mesoscale and Nanoscale Physics Traditional edge detection algorithms, foundational to computer vision, face significant challenges in energy efficiency and processing latency on conventional CMOS-based hardware. Existing algorithms, such as Canny, are computationally expensive, posing challenges in resource-constrained hardware where energy efficiency and low latency are critical. This study introduces a novel, hardware-efficient algorithm that leverages the intrinsic characteristics of magnetic tunnel junction (MTJ) devices. We present a detailed device-level analysis of an MTJ-based system for edge detection, outlining its operational cycles, including write, read, and reset methods. The algorithm's efficacy is evaluated against the standard Canny edge detection method. We provide a quantitative performance analysis, including metrics such as energy consumption and latency, which demonstrates that our proposed spintronics-based approach offers a promising solution for achieving low-power, high-speed image processing. |
| title | Edge Detection Framework Utilizing SOT-MTJ Bit-Cell Arrays |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2606.00744 |