Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Predictive dynamic thermal management for multimedia applications
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
Temperature-aware microarchitecture: Modeling and implementation
ACM Transactions on Architecture and Code Optimization (TACO)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Stochastic modeling of a thermally-managed multi-core system
Proceedings of the 45th annual Design Automation Conference
Hybrid dynamic thermal management based on statistical characteristics of multimedia applications
Proceedings of the 13th international symposium on Low power electronics and design
Temperature-constrained power control for chip multiprocessors with online model estimation
Proceedings of the 36th annual international symposium on Computer architecture
Dynamic thermal management via architectural adaptation
Proceedings of the 46th Annual Design Automation Conference
Adaptive power management using reinforcement learning
Proceedings of the 2009 International Conference on Computer-Aided Design
Utilizing predictors for efficient thermal management in multiprocessor SoCs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Consistent runtime thermal prediction and control through workload phase detection
Proceedings of the 47th Design Automation Conference
Thermal aware task sequencing on embedded processors
Proceedings of the 47th Design Automation Conference
Temperature-aware dynamic resource provisioning in a power-optimized datacenter
Proceedings of the Conference on Design, Automation and Test in Europe
Proceedings of the International Conference on Computer-Aided Design
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Multimedia applications are expected to form the largest portion of workload in general purpose PC and portable devices. The ever-increasing computation intensity of multimedia applications elevates the processor temperature and consequently impairs the reliability and performance of the system. In this paper, we propose to perform dynamic thermal management using reinforcement learning algorithm for multimedia applications. The proposed learning model does not need any prior knowledge of the workload information or the system thermal and power characteristics. It learns the temperature change and workload switching patterns by observing the temperature sensor and event counters on the processor, and finds the management policy that provides good performance-thermal tradeoff during the runtime. We validated our model on a Dell personal computer with Intel Core 2 processor. Experimental results show that our approach provides considerable performance improvements with marginal increase in the percentage of thermal hotspot comparing to existing workload phase detection approach.