Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90105
Title: Chip temperature optimization for dark silicon many-core systems
Authors: Li, Mengquan
Liu, Weichen
Yang, Lei
Chen, Peng
Chen, Chao
Keywords: Dark Silicon
Chip Temperature Optimization
DRNTU::Engineering::Computer science and engineering
Issue Date: 2017
Source: Li, M., Liu, W., Yang, L., Chen, P., & Chen, C. (2018). Chip temperature optimization for dark silicon many-core systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(5), 941-953. doi:10.1109/TCAD.2017.2740306
Series/Report no.: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Abstract: In the dark silicon era, a fundamental problem is given a real-time computation demand, how to determine if an on-chip multiprocessor system is able to accept this demand and to maintain its reliability by keeping every core within a safe temperature range. In this paper, a practical thermal model is described for quick chip temperature prediction. Integrated with the thermal model, we present a mixed integer linear programming (MILP) model to find the optimal task-to-core assignment with the minimum chip peak temperature. For the worst case where even the minimum chip peak temperature exceeds the safe temperature, a heuristic algorithm, called temperature-constrained task selection (TCTS), is proposed to optimize the system performance within chip safe temperature. The optimality of the TCTS algorithm is formally proven. Extensive performance evaluations show that our thermal model achieves an average prediction accuracy of 0.0741 °C within 0.2392 ms. The MILP model reduces chip peak temperature of ~10 °C comparing with traditional techniques. The system performance is increased by 19.8% under safe temperature limitation. Due to the satisfying scalability of our MILP formulation, the chip peak temperature is further decreased by 5.06 °C via the TCTS algorithm. The feasibility of this systematical technique is testified in a real case study as well.
URI: https://hdl.handle.net/10356/90105
http://hdl.handle.net/10220/48374
ISSN: 0278-0070
DOI: http://dx.doi.org/10.1109/TCAD.2017.2740306
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCAD.2017.2740306.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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