Hot Spot Identification and System Parameterized Thermal Modeling for Multi-Core Processors Through Infrared Thermal Imaging
Sheriff Sadiqbatcha1, Hengyang Zhao1, Hussam Amrouch2, Jörg Henkel2 and Sheldon X.-D. Tan1
1University of California, Riverside, CA, USA
2Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
ABSTRACT
Accurate thermal models suitable for system level dynamic thermal, power and reliability regulation and management are vital for many commercial multi-core processors. However, developing such accurate thermal models and identifying the related thermal-power relevant spatial locations for commercial processors is a challenging task due to the lack of information and available tools. Existing tools such as HotSpot-like thermal models may suffer from inaccuracy or inefficiency for online applications, primarily because most rely on parameters that cannot be precisely quantified, such as power-traces, while others are numerical methods not suitable for runtime use. In this work, we propose a novel approach to automatically detecting the major heat-sources on a commercial multi-core microprocessor using an infrared thermal imaging setup. Our approach involves a number of steps including 2D discrete cosine transformation filter for noise reduction on the measured thermal maps, and Laplacian transformation followed by K-mean clustering for heat-source identification. Since the identified heat-sources are the thermally vulnerable areas of the die, we propose a novel approach to deriving a thermal model capable of predicting their temperatures during runtime. We apply Long-Short-Term-Memory (LSTM) networks to build a dynamic thermal model which uses systemlevel variables such as chip frequency, voltage and instruction count as inputs. The model is trained and tested exclusively using measured thermal data from a commercial multi-core processor. Experimental results show that the proposed thermal model achieves very high accuracy (root-mean-square-error: 2.04°C to 2.57°C) in predicting the temperature of all the identified heatsources on the chip.
