Edge Detection Framework Utilizing SOT-MTJ Bit-Cell Arrays

Abstract

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.