Abstract
STT-MRAM is an emerging non-volatile memory quickly approaching DRAM in terms of capacity, frequency and device size. Intensified efforts in STT-MRAM research by the memory manufacturers may indicate a revolution with STT-MRAM memory technology is imminent, and therefore it is essential to perform system level research to explore use-cases and identify computing domains that could benefit from this technology. Special STT-MRAM features such as intrinsic radiation hardness, non-volatility, zero stand-by power and capability to function in extreme temperatures makes it particularly suitable for aerospace, avionics and automotive applications. Such applications often have real-time requirements — that is, certain tasks must complete within a strict deadline. Analyzing whether this deadline is met requires Worst Case Execution Time (WCET) Analysis, which is a fundamental part of evaluating any real-time system. In this study, we investigate the feasibility of using STT-MRAM in real-time embedded systems by analyzing average system performance impact and WCET implications.
Original language | English |
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Title of host publication | MEMSYS 2019 - Proceedings of the International Symposium on Memory Systems |
Publisher | Association for Computing Machinery |
Pages | 195-205 |
Number of pages | 11 |
ISBN (Electronic) | 9781450372060 |
DOIs | |
State | Published - Sep 30 2019 |
Externally published | Yes |
Event | 2019 International Symposium on Memory Systems, MEMSYS 2019 - Washington, United States Duration: Sep 30 2019 → Oct 3 2019 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 2019 International Symposium on Memory Systems, MEMSYS 2019 |
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Country/Territory | United States |
City | Washington |
Period | 09/30/19 → 10/3/19 |
Funding
This work was supported by BSC, Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under ExaNoDe project (grant agreement No 671578). Jaume Abella was partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship RYC-2013-14717.