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  1. Proceedings of the 8th Workshop and 6th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM '17)
  2. On Boosting Energy-Efficiency of Heterogeneous Embedded Systems via Game Theory
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Dataflow Acceleration of scikit-learn Gaussian Process Regression
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VectorPU: A Generic and Efficient Data-container and Component Model for Transparent Data Transfer on GPU-based Heterogeneous Systems
MTP-Caffe: Memory, Timing, and Power aware tool for mapping CNNs to GPUs
Using PEGs for Automatic Extraction of Memory Access Descriptions to Support Data-Parallel Pattern Recognition
On Boosting Energy-Efficiency of Heterogeneous Embedded Systems via Game Theory

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On Boosting Energy-Efficiency of Heterogeneous Embedded Systems via Game Theory

Content Provider ACM Digital Library
Author Pereira, David Ilic, Aleksandar Sousa, Leonel
Abstract In this paper, a novel game theory based approach for task scheduling on emerging heterogeneous embedded systems is proposed. It relies on the auction concept to assign tasks to players, where players compete against each other by bidding for the tasks in order to acquire them. To ensure the feasibility of game rounds, a set of different utility functions are formulated, such that each player can select its own best strategy, which corresponds to the frequency level that guarantees the minimum energy consumption variation at the overall system level. The proposed energy-aware multiplayer auction-based scheduling approach was extensively evaluated across a set of 13 real applications from 4 standard benchmark suites on the state-of-the-art ARM Juno r2 platform with two different multi-core clusters as composite players, i.e, dual-core Cortex-A72 and a quad-core Cortex-A53 clusters. For different benchmark combinations, the experimental results show that the proposed approach allows achieving significant energy savings when compared to the standard ARM Linaro, GTS and EAS approaches, i.e., up to 36%, 32% and 22%, respectively.
Starting Page 19
Ending Page 24
Page Count 6
File Format PDF
ISBN 9781450348775
DOI 10.1145/3029580.3029584
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-01-25
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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