CRAVE: Analyzing Cross-Resource Interaction to Improve Energy Efficiency in Systems-on-Chip

Mobile platforms make use of dynamic voltage and frequency scaling (DVFS) to trade off runtime performance and power consumption for their systems-on-chip (SoCs). State-of-the-art governors in the OS use application-based characteristics to control the SoC's DVFS settings for CPU cores, as well as the GPU in some SoCs. Through experimental characterization of real-world mobile platforms, we find that key SoC components have a complex relationship with one another, which directly affects their performance and power usage. This relationship is dependent on the architecture of the SoC as it is caused by the interaction of processing elements such as the CPU and GPU through a shared main memory. Unfortunately, existing application-oriented governors do not explicitly capture this design-induced relationship.

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Publication Details

Published
2025-03-26
DOI
https://doi.org/10.1145/3689031.3717498
Citations
2
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
4.54
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CRAVE: Analyzing Cross-Resource Interaction to Improve Energy Efficiency in Systems-on-Chip

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2 citations
Parallel Computing and Optimization Techniques
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article

CRAVE: Analyzing Cross-Resource Interaction to Improve Energy Efficiency in Systems-on-Chip

Jiaji Bao, Saugata Ghose, Curtis Madsen, Gul Agha, Dipayan Mukherjee, Tian Ma, Sam Hachem
article en
2 citations

Abstract

Mobile platforms make use of dynamic voltage and frequency scaling (DVFS) to trade off runtime performance and power consumption for their systems-on-chip (SoCs). State-of-the-art governors in the OS use application-based characteristics to control the SoC's DVFS settings for CPU cores, as well as the GPU in some SoCs. Through experimental characterization of real-world mobile platforms, we find that key SoC components have a complex relationship with one another, which directly affects their performance and power usage. This relationship is dependent on the architecture of the SoC as it is caused by the interaction of processing elements such as the CPU and GPU through a shared main memory. Unfortunately, existing application-oriented governors do not explicitly capture this design-induced relationship.

University of Illinois Urbana-Champaign (US), Sandia National Laboratories (US)
Affordable and clean energy
Openalex Percentile: Top 8%
Parallel Computing and Optimization Techniques
4.54
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