Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
HybDTM: a coordinated hardware-software approach for dynamic thermal management
Proceedings of the 43rd annual Design Automation Conference
Complexity-based program phase analysis and classification
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Spectral analysis for characterizing program power and performance
ISPASS '04 Proceedings of the 2004 IEEE International Symposium on Performance Analysis of Systems and Software
Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture
Physical aware frequency selection for dynamic thermal management in multi-core systems
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Efficient power modeling and software thermal sensing for runtime temperature monitoring
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Analysis of dynamic voltage/frequency scaling in chip-multiprocessors
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
Thermal response to DVFS: analysis with an Intel Pentium M
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
Predictive dynamic thermal management for multicore systems
Proceedings of the 45th annual Design Automation Conference
Proactive temperature management in MPSoCs
Proceedings of the 13th international symposium on Low power electronics and design
Discovering and Exploiting Program Phases
IEEE Micro
Utilizing predictors for efficient thermal management in multiprocessor SoCs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
System-Level Dynamic Thermal Management for High-Performance Microprocessors
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Dynamic thermal management for multimedia applications using machine learning
Proceedings of the 48th Design Automation Conference
QoS optimization for thermal-aware cyber-physical systems
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Full-chip runtime error-tolerant thermal estimation and prediction for practical thermal management
Proceedings of the International Conference on Computer-Aided Design
Post-silicon power mapping techniques for integrated circuits
Integration, the VLSI Journal
Online thermal control methods for multiprocessor systems
ACM Transactions on Design Automation of Electronic Systems (TODAES) - Special section on adaptive power management for energy and temperature-aware computing systems
Thermal prediction and adaptive control through workload phase detection
ACM Transactions on Design Automation of Electronic Systems (TODAES) - Special section on adaptive power management for energy and temperature-aware computing systems
SCC thermal model identification via advanced bias-compensated least-squares
Proceedings of the Conference on Design, Automation and Test in Europe
A multi-agent framework for thermal aware task migration in many-core systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Performance-driven dynamic thermal management of MPSoC based on task rescheduling
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Dynamic Power and Thermal Management of NoC-Based Heterogeneous MPSoCs
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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Elevated temperatures impact the performance, power consumption, and reliability of processors, which rely on integrated thermal sensors to measure runtime thermal behavior. These thermal measurements are typically inputs to a dynamic thermal management system that controls the operating parameters of the processor and cooling system. The ability to predict future thermal behavior allows a thermal management system to optimize a processor's operation so as to prevent the on-set of high temperatures. In this paper we propose a new thermal prediction method that leads to consistent results between the thermal models used in prediction and observed thermal sensor measurements, and is capable of accurately predicting temperature behavior with heterogenous workload assignment on a multicore platform. We devise an off-line analysis algorithm that learns a set of thermal models as a function of operating frequency and globally defined workload phases. We incorporate these thermal models into a dynamic voltage and frequency scaling (DVFS) technique that limits the maximum temperature during runtime. We demonstrate the effectiveness of our proposed system in predicting the thermal behavior of a real quad-core processor in response to different workloads. In comparison to a reactive thermal management technique, our predictive method dramatically reduces the number of thermal violations, the magnitude of thermal cycles, and workload runtimes.