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P-EdgeCoolingMode: an agent-based performance aware thermal management unit for DVFS enabled heterogeneous MPSoCs
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P-EdgeCoolingMode: an agent-based performance aware thermal management unit for DVFS enabled heterogeneous MPSoCs
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Journal Article
P-EdgeCoolingMode: an agent-based performance aware thermal management unit for DVFS enabled heterogeneous MPSoCs
2019
Overview
Thermal cycling, as well as spatial and thermal gradient, affects the lifetime reliability and performance of heterogeneous Multi-Processor Systems-on-Chips (MPSoCs). Conventional temperature management techniques are not intelligent enough to cater for performance, energy efficiency as well as the operating temperature of the system. In this study, the authors propose a light-weight novel thermal management mechanism (P-EdgeCoolingMode) in the form of intelligent software agent, which monitors and regulates the operating temperature of the CPU cores to improve the reliability of the system while catering for performance requirements. P-EdgeCoolingMode is capable of pro-actively monitoring performance and based on the user's demand the agent takes necessary action, making the proposed methodology highly suitable for implementation on existing as well as conceptual Edge devices utilising heterogeneous MPSoCs with dynamic voltage and frequency scaling (DVFS) capabilities. They validated the authors’ methodology on the Odroid-XU4 MPSoC and Huawei P20 Lite (HiSilicon Kirin 659 MPSoC). P-EdgeCoolingMode has been successful in reducing the operating temperature while improving performance and reducing power consumption for chosen test cases than the state-of-the-art. For applications with demanding performance requirement P-EdgeCoolingMode has been found to improve the power consumption by 30.62% at the most in comparison to existing state-of-the-art power management methodologies.
Publisher
The Institution of Engineering and Technology,John Wiley & Sons, Inc
Subject
agent‐based performance aware thermal management unit
/ cooling
/ DVFS
/ dynamic voltage and frequency scaling capabilities
/ heterogeneous multiprocessor systems‐on‐chips
/ light‐weight thermal management mechanism
/ Linux
/ Software
/ Special Issue: Energy-efficient Computing for Embedded and IoT Devices
