Approximate MRAM: High-Performance and Power-Efficient Computing With MRAM Chips for Error-Tolerant Applications

• Published in: IEEE transactions on computers Vol. 72; no. 3; pp. 668 - 681
• Main Authors: Ferdaus, Farah, Talukder, B. M. S. Bahar, Rahman, Md Tauhidur
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximate computing; approximate MRAM; cache write policy; Computation; Computer architecture; Computer memory; Computer vision; Errors; Image compression; low power design; Machine learning; Magnetic tunneling; Memory management; Microprocessors; MRAM; Multimedia; Power consumption; Random access memory; Resilience; Signal processing; Spintronics; Thermal stability; Tunnel junctions
• ISSN: 0018-9340; 1557-9956
• DOI: 10.1109/TC.2022.3174584

Approximate computing (AC) leverages the inherent error resilience and is used in many big-data applications from various domains such as multimedia, computer vision, signal processing, and machine learning to improve systems performance and power consumption. Like many other approximate circuits and algorithms, the memory subsystem can also be used to enhance performance and save power significantly. This paper proposes an efficient and effective systematic methodology to construct an approximate non-volatile magneto-resistive RAM (MRAM) framework using consumer-off-the-shelf (COTS) MRAM chips. In the proposed scheme, an extensive experimental characterization of memory errors is performed by manipulating the write latency of MRAM chips which exploits the inherent (intrinsic/extrinsic process variation) stochastic switching behavior of magnetic tunnel junctions (MTJs). The experimental results, involving error-resilient image compression and machine learning applications, reveal that the proposed AC framework provides a significant performance improvement and demonstrates a reduction in MRAM write energy of <inline-formula><tex-math notation="LaTeX">{\sim }47.5\%</tex-math> <mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>47</mml:mn><mml:mo>.</mml:mo><mml:mn>5</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="ferdaus-ieq1-3174584.gif"/> </inline-formula> on average with negligible or no loss in output quality.