Paper Info
Title
Robust and Accurate Fine-Grain Power Models for Embedded Systems With No On-Chip PMU
Abstract
This letter a novel approach to event-based power modeling for embedded platforms that do not have a performance monitoring unit (PMU). The method involves complementing the target hardware platform, where the physical power data is measured, with another platform on which the CPU performance data, that is needed for model generation, can be collected. The methodology is used to generate accurate fine-grain power models for the Gaisler GR712RC dual-core LEON3 fault-tolerant SPARC processor with onboard power sensors and no PMU. A Kintex UltraScale field-programmable gate array (FPGA) is used as the support platform to obtain the required CPU performance data, by running a soft-core representation of the dual-core LEON3 as on the GR712RC but with a PMU implementation. Both platforms execute the same benchmark set and data collection is synchronized using per-sample timestamps so that the power sensor data from the GR712RC board can be matched to the PMU data from the FPGA. The synchronized samples are then processed by the Robust Energy and Power Predictor Selection (REPPS) software in order to generate power models. The models achieve less than 2% power estimation error when validated on an industrial use case and can follow program phases, which makes them suitable for runtime power profiling during development.
Year
DOI
Venue
2022
10.1109/LES.2022.3147308
IEEE Embedded Systems Letters
Keywords
DocType
Volume
Application-specified integrated circuit (ASIC),field-programmable gate array (FPGA),LEON3,performance monitoring counter (PMC),power models
Journal
14
Issue
ISSN
Citations 
3
1943-0663
0
PageRank 
References 
Authors
0.34
10
7
Name
Order
Citations
PageRank
Kris Nikov100.34
Marcos Martinez200.34
Simon Wegener300.68
Jose Nunez-Yanez400.34
Zbigniew Chamski500.34
Kyriakos Georgiou612.46
Kerstin Eder700.34