Power Efficient Image Processing with TMR Tunable Hybrid Approximate Adders

Gulafshan Gulafshan; Rajat Kumar; Danial Khan; Selma Amara; Yehia Massoud

doi:10.1109/NANO58406.2023.10231223

Power Efficient Image Processing with TMR Tunable Hybrid Approximate Adders

Gulafshan Gulafshan, Rajat Kumar, Danial Khan, Selma Amara, Yehia Massoud^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

An emerging spintronic device, magnetic tunnel junction (MTJ) mitigates the challenges like leakage power, high dynamic power as well as static power, faced by present CMOS technology. The non-volatility offers extra power saving during idle state by completely shut-down a MTJ based circuits without losing any data or extra hardware. Further, an approximate computing is a reliable technique for improving significant performance in trade of accuracy. In this paper, two fully non-volatile, 1-bit approximate adders are proposed with error distance in sum is two, extended to 12-bit hybrid adder in which a mix of approximate adders are placed on lower significant bits (LSB) and exact adder (EXA3) is placed on higher significant bits, and these hybrid adders are employed in image processing (implementing Gaussian filter) for removing noise. It is observed that the proposed adder (AXMFA1) is 23 % power efficient and reduces delay by 21 % by slightly compromising the accuracy (maximum error distance is 5) that implies the degradation in image quality: reduction of PSNR and SSIM by 0.13% and 0.28%.

Original language	English (US)
Title of host publication	2023 IEEE 23rd International Conference on Nanotechnology, NANO 2023
Publisher	IEEE Computer Society
Pages	556-561
Number of pages	6
ISBN (Electronic)	9798350333466
DOIs	https://doi.org/10.1109/NANO58406.2023.10231223
State	Published - 2023
Event	23rd IEEE International Conference on Nanotechnology, NANO 2023 - Jeju City, Korea, Republic of Duration: Jul 2 2023 → Jul 5 2023

Publication series

Name	Proceedings of the IEEE Conference on Nanotechnology
Volume	2023-July
ISSN (Print)	1944-9399
ISSN (Electronic)	1944-9380

Conference

Conference	23rd IEEE International Conference on Nanotechnology, NANO 2023
Country/Territory	Korea, Republic of
City	Jeju City
Period	07/2/23 → 07/5/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

Bioengineering
Electrical and Electronic Engineering
Materials Chemistry
Condensed Matter Physics

Access to Document

10.1109/NANO58406.2023.10231223

Cite this

Gulafshan, G., Kumar, R., Khan, D., Amara, S., & Massoud, Y. (2023). Power Efficient Image Processing with TMR Tunable Hybrid Approximate Adders. In 2023 IEEE 23rd International Conference on Nanotechnology, NANO 2023 (pp. 556-561). (Proceedings of the IEEE Conference on Nanotechnology; Vol. 2023-July). IEEE Computer Society. https://doi.org/10.1109/NANO58406.2023.10231223

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title = "Power Efficient Image Processing with TMR Tunable Hybrid Approximate Adders",

abstract = "An emerging spintronic device, magnetic tunnel junction (MTJ) mitigates the challenges like leakage power, high dynamic power as well as static power, faced by present CMOS technology. The non-volatility offers extra power saving during idle state by completely shut-down a MTJ based circuits without losing any data or extra hardware. Further, an approximate computing is a reliable technique for improving significant performance in trade of accuracy. In this paper, two fully non-volatile, 1-bit approximate adders are proposed with error distance in sum is two, extended to 12-bit hybrid adder in which a mix of approximate adders are placed on lower significant bits (LSB) and exact adder (EXA3) is placed on higher significant bits, and these hybrid adders are employed in image processing (implementing Gaussian filter) for removing noise. It is observed that the proposed adder (AXMFA1) is 23 % power efficient and reduces delay by 21 % by slightly compromising the accuracy (maximum error distance is 5) that implies the degradation in image quality: reduction of PSNR and SSIM by 0.13% and 0.28%.",

author = "Gulafshan Gulafshan and Rajat Kumar and Danial Khan and Selma Amara and Yehia Massoud",

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doi = "10.1109/NANO58406.2023.10231223",

language = "English (US)",

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Gulafshan, G, Kumar, R , Khan, D , Amara, S & Massoud, Y 2023, Power Efficient Image Processing with TMR Tunable Hybrid Approximate Adders. in 2023 IEEE 23rd International Conference on Nanotechnology, NANO 2023. Proceedings of the IEEE Conference on Nanotechnology, vol. 2023-July, IEEE Computer Society, pp. 556-561, 23rd IEEE International Conference on Nanotechnology, NANO 2023, Jeju City, Korea, Republic of, 07/2/23. https://doi.org/10.1109/NANO58406.2023.10231223

Power Efficient Image Processing with TMR Tunable Hybrid Approximate Adders. / Gulafshan, Gulafshan; Kumar, Rajat ; Khan, Danial et al.
2023 IEEE 23rd International Conference on Nanotechnology, NANO 2023. IEEE Computer Society, 2023. p. 556-561 (Proceedings of the IEEE Conference on Nanotechnology; Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Power Efficient Image Processing with TMR Tunable Hybrid Approximate Adders

Abstract

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