Computer Science > Emerging Technologies
[Submitted on 29 May 2021 (v1), last revised 14 Mar 2022 (this version, v2)]
Title:Approximate MRAM: High-performance and Power-efficient Computing with MRAM Chips for Error-tolerant Applications
View PDFAbstract: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 and error-resilient image application reveal that the proposed AC framework provides a significant performance improvement and demonstrates a maximum reduction in MRAM write current of ~66% on average with negligible or no loss in output quality.
Submission history
From: Farah Ferdaus [view email][v1] Sat, 29 May 2021 00:11:00 UTC (3,822 KB)
[v2] Mon, 14 Mar 2022 16:59:38 UTC (7,813 KB)
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