Machine learning based workload balancing scheme for minimizing stress migration induced aging in multicore processors-Reference-Cited by-同舟云学术

Machine learning based workload balancing scheme for minimizing stress migration induced aging in multicore processors

Published:2022-09-24 Issue:1 Volume:15 Page:399-410
ISSN:2511-2104
Container-title:International Journal of Information Technology
language:en
Short-container-title:Int. j. inf. tecnol.

Author:

Kumar P. Jagadeesh,Mini M. G.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s41870-022-01105-6.pdf

Reference42 articles.

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2. Sharifi F, Rohbani N, Hessabi S (2020) Aging-aware context switching in multicore processors based on workload classification. In: IEEE computer architecture letters, 19(2):159–162. https://doi.org/10.1109/LCA.2020.3040326

3. Sahoo SS, Behnaz R, Akash K (2021) Reliability-aware resource management in multi-/many-core systems: a perspective paper. J Low Power Electron Appl 11:7

4. Ma Y, Zhou J, Chantem T, Dick RP, Hu XS (2021) Resource management for improving overall reliability of multi-processor systems-on-chip. In: Henkel J, Dutt N (eds) Dependable embedded systems, Springer, Cham. https://doi.org/10.1007/978-3-030-52017-5_10

5. McPherson JW (2018) Brief history of JEDEC qualification standards for silicon technology and their applicability (?) to WBG semiconductors. In: 2018 IEEE International Reliability Physics Symposium (IRPS), pp 3B-1, IEEE, 2018

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