Clustering and Symbolic Regression for Power Consumption Estimation on Smartphone Hardware Subsystems

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@Article{Rattagan:2018:ieeeSusComp,

• author = "Ekarat Rattagan and Ying-Dar Lin and Yuan-Cheng Lai and Edward T.-H. Chu and Kate Ching-Ju Lin",
• journal = "IEEE Transactions on Sustainable Computing",
• title = "Clustering and Symbolic Regression for Power Consumption Estimation on Smartphone Hardware Subsystems",
• year = "2018",
• volume = "3",
• number = "4",
• pages = "306--317",
• abstract = "The subsystem in a smart phone means its hardware components, such as the CPU, GPU, and screen. Accurately estimating subsystem power consumption of commercial smartphones is necessary for applicable to wide research areas. Current subsystem power estimation techniques are mostly based on power models, resulting in considerable errors for various types of power consumption behaviours. These include (1) asynchrony between the measured power consumption and the corresponding workload statistics, and (2) nonlinearity concerning CPU idle states, pixels colours of AMOLED screen, and GPU workload statistics. In this study, we propose a novel usage-based, subsystem power estimation method for a smartphone, namely Clustering and Symbolic Regression (CSR) that takes these power consumption behaviours into account so as to increase power estimation accuracy. To address asynchrony, we cluster the subsystem workload statistics into synchronous and asynchronous groups by employing affinity propagation clustering. To address nonlinearity, we employ symbolic regression for fitting measured power consumptions with respect to subsystem workload statistics. We compare our approach with various power estimation methods, Linear Regression Model (LM), Genetic Programming (GP), and Support Vector Regression (SVR). The results show Mean Absolute Percentage Error (MAPE) reduction between 23.61 and 42.55 percent on the estimated power consumption of a simple (Nexus S) and complex (Galaxy S4) smartphone subsystems.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/TSUSC.2018.2832173",
• ISSN = "2377-3782",
• month = oct,
• notes = "Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan

  Also known as \cite{8360076}",

}

Genetic Programming entries for Ekarat Rattagan Ying-Dar Lin Yuan-Cheng Lai Edward T-H Chu Kate Ching-Ju Lin

Citations