JOURNAL ARTICLE
Performance Analysis of Spin Orbit Torque Magneto-Resistive RAM Caches in 4-core ARM Systems.
Published In: Journal of Circuits, Systems & Computers, 2025, v. 34, n. 3. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Singh, Inderjit; Raj, Balwinder; Khosla, Mamta 3 of 3
Abstract
Spin Orbit Torque Magnetic Random Access Memory (SOT–)MRAM is gaining interest as it eradicates several limitations posed by its predecessor Spin Transfer Torque (STT-)MRAM, yet inherits all its advantages. This work explores in detail, the suitability of SOT–MRAM implemented caches in different levels of memory hierarchy in comparison to conventional SRAM technology, over several performance parameters like area, energy consumption and execution time for an embedded benchmark suite. Our circuit-level analysis shows that SOT–MRAM outperforms SRAM for caches (> 1 2 8 KB), and only lags in area and read-access energy for smaller caches. A typical 512 KB SOT–MRAM cache improves area by 1%, read/write latency by 33/38%, and leakage by over 99% than that of SRAM memory technology. The architecture-level analysis confirms that on average SOT–MRAM is energy efficient by 74% in L1, 97.2% in L2 and 89.3% in both (i.e., L1 + L2) implementations against SRAM, for a 22 nm technology node. We also estimate that SOT–MRAM only solution offers ∼ 6 8. 8 % energy savings and ∼ 7 9. 5 % better EDP than Hybrid (L1-SRAM and L2-SOT) memory hierarchy for multi-core ARM processors. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Circuits, Systems & Computers. 2025/02, Vol. 34, Issue 3, p1
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2025
- ISSN:0218-1266
- DOI:10.1142/S0218126625500719
- Accession Number:183762421
- Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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