Temperature-aware microarchitecture: Modeling and implementation
ACM Transactions on Architecture and Code Optimization (TACO)
Convex Optimization
Exploring "temperature-aware" design in low-power MPSoCs
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Techniques for Multicore Thermal Management: Classification and New Exploration
Proceedings of the 33rd annual international symposium on Computer Architecture
Physical aware frequency selection for dynamic thermal management in multi-core systems
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Continuous Frequency Adjustment Technique Based on Dynamic Workload Prediction
VLSID '08 Proceedings of the 21st International Conference on VLSI Design
Temperature management in multiprocessor SoCs using online learning
Proceedings of the 45th annual Design Automation Conference
Temperature control of high-performance multi-core platforms using convex optimization
Proceedings of the conference on Design, automation and test in Europe
Proactive temperature balancing for low cost thermal management in MPSoCs
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Fast Computation of Optimal Contact Forces
IEEE Transactions on Robotics
Convex-based thermal management for 3D MPSoCs using DVFS and variable-flow liquid cooling
PATMOS'11 Proceedings of the 21st international conference on Integrated circuit and system design: power and timing modeling, optimization, and simulation
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
Hi-index | 0.00 |
Meeting the temperature constraints and reducing the hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. The goal of thermal management is to meet maximum operating temperature constraints, while tracking timevarying performance requirements. Current approaches avoid thermal violations by forcing abrupt operating points changes, which cause sharp performance degradation. In this paper we aim at achieving an online smooth thermal control action, that minimizes the tracking error. We formulate this problem as a discrete-time optimal control problem, which can be solved via online by using an embedded convex optimization solver using a receding horizon approach. The optimization problem considers the thermal profile of the system, its evolution over time, current and past time-varying workload requirements. We perform experiments on a model of the 8-core Niagara-1 multicore architecture, which show that the proposed method outperforms state-of-the-art thermal management approaches by enabling performance speed-ups of up to 2.5x and improvements up to 12x and 3.4x in relation to frequency and temperature variations over time, respectively.