APA
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Aravind, N. V. N., Verma, G., Soni, S., Shukla, A. K., Sehgal, A., Roy, S., & Kaushik, B. K. (2024). Image Edge Extraction Using SOT-MRAM Based In-Memory Computing. Nanotechnology Materials and Devices Conference.
Chicago/Turabian
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Aravind, Neelathi Venkata Naga, G. Verma, Sandeep Soni, Alok Kumar Shukla, Anubha Sehgal, Sourajeet Roy, and Brajesh Kumar Kaushik. “Image Edge Extraction Using SOT-MRAM Based In-Memory Computing.” Nanotechnology Materials and Devices Conference (2024).
MLA
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Aravind, Neelathi Venkata Naga, et al. “Image Edge Extraction Using SOT-MRAM Based In-Memory Computing.” Nanotechnology Materials and Devices Conference, 2024.
BibTeX Click to copy
@article{neelathi2024a,
title = {Image Edge Extraction Using SOT-MRAM Based In-Memory Computing},
year = {2024},
journal = {Nanotechnology Materials and Devices Conference},
author = {Aravind, Neelathi Venkata Naga and Verma, G. and Soni, Sandeep and Shukla, Alok Kumar and Sehgal, Anubha and Roy, Sourajeet and Kaushik, Brajesh Kumar}
}
Edge extraction is a crucial task in the realms of computer vision and image processing. Numerous algorithms for edge extraction traditionally depend on computationally intensive computations involving first-order or second-order derivatives, which, in turn, demand substantial energy consumption. Consequently, there is a need to explore other alternate methods that can be implemented in-memory and thereby reducing energy consumption. In this work, we explore a novel method of efficient edge detection using in-memory computing with spin-orbit torque magnetic random-access memory (SOT-MRAM). The paper presents a 4 × 1 SOT-MRAM array implementing XOR operations that can be used for extracting edges with 2 × 2 sub-matrices of a binary image followed by voltage comparisons. The proposed method targets low power consumption with minimal circuitry and shows a slight variation in performance metrics as compared to conventional approach of MRAM based edge detection operators such as Sobel, Canny Prewitt and Roberts.