Proceedings of the 49th Annual Design Automation Conference
A power-driven thermal sensor placement algorithm for dynamic thermal management
Proceedings of the Conference on Design, Automation and Test in Europe
An efficient method for analyzing on-chip thermal reliability considering process variations
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Eagle-eye: a near-optimal statistical framework for noise sensor placement
Proceedings of the International Conference on Computer-Aided Design
| Hi-index | 14.98 |
Hot spots are a major concern in high-end processors as they constrain performance and limit the lifetime of semiconductor chips. Using embedded thermal sensors, dynamic thermal management systems track the hot spots during runtime and adjust the performance and the cooling system of the processor when necessary. In many-core processors, the locations of hot spots vary spatially and temporally depending on the configuration of active cores and the workloads running on the cores. Our work includes both theoretical advances in sensor allocation techniques and experimental advances for thermal imaging of real processors. We propose a hard sensor allocation algorithm to determine the sensor locations where hot spots can be tracked accurately given a budget number of sensors. We also propose soft sensor computation techniques to alleviate design constraints on sensor locations and to further improve the resolution of hot spot tracking. The proposed soft sensing technique combines the measurements of the hard sensors in an optimal way to estimate the temperature at any desired location. We use infrared imaging methods to characterize the thermal behavior of a real dual-core processor during operation. We execute large number of workload configurations on the processor and track the locations and temperatures of hot spots during runtime. The thermal characterization data are then used as the input to our sensor allocation techniques. We demonstrate that our sensor allocation techniques improve significantly upon the previous results in the literature and provide accurate tracking of hot spots.