You are in:Home/Publications/Mostafa E. A. Ibrahim, Markus Rupp, and Hossam A. H. Fahmy. A Precise High-Level Power Consumption Model for Embedded Systems Software. EURASIP Journal on Embedded Systems Volume 2011 (2011), Article ID 480805, 14 pages doi:10.1155/2011/480805.

Dr. Mostafa Elsayed Ahmed Ibrahim :: Publications:

Title:
Mostafa E. A. Ibrahim, Markus Rupp, and Hossam A. H. Fahmy. A Precise High-Level Power Consumption Model for Embedded Systems Software. EURASIP Journal on Embedded Systems Volume 2011 (2011), Article ID 480805, 14 pages doi:10.1155/2011/480805.
Authors: Mostafa E. A. Ibrahim, Markus Rupp, and Hossam A. H. Fahmy
Year: 2015
Keywords: Not Available
Journal: EURASIP Journal on Embedded Systems Volume 2011,
Volume: 2011
Issue: Not Available
Pages: 14 pages
Publisher: EURASIP
Local/International: International
Paper Link: Not Available
Full paper Not Available
Supplementary materials Not Available
Abstract:

The increasing demand for portable computing has elevated power consumption to be one of the most critical embedded systems design parameters. In this paper, we present a precise high-level power estimation methodology for the software loaded on a VLIW processor that is based on a functional level power model. The targeted processor of our approach is the TMS320C6416T DSP from Texas Instrument.We consider several important issues in our model such as the pipeline stall, inter-instructions effect and cache misses. The contributions are the following. First, a precise model to estimate the power consumption of the targeted DSP, while running a software algorithm is proposed. Second, we prove the validation and precision of our model on many typical algorithms applied in signal and image processing. Third, we further validate the precision of our model on a real application applied in the video processing field. The power consumption estimated by our model is compared to the physically measured power consumption, achieving a very low average absolute estimation error of 1.65% and a maximum absolute estimation error of only 3.3%.

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