Title
Hybrid functional and instruction level power modeling for embedded processors
Abstract
In this contribution the concept of Functional-Level Power Analysis (FLPA) for power estimation of programmable processors is extended in order to model even embedded general purpose processors. The basic FLPA approach is based on the separation of the processor architecture into functional blocks like e.g. processing unit, clock network, internal memory etc. The power consumption of these blocks is described by parameterized arithmetic models. By application of a parser based automated analysis of assembler codes the input parameters of the arithmetic functions like e.g. the achieved degree of parallelism or the kind and number of memory accesses can be computed. For modeling an embedded general purpose processor (here, an ARM940T) the basic FLPA modeling concept had to be extended to a so-called hybrid functional level and instruction level model in order to achieve a good modeling accuracy. The approach is exemplarily demonstrated and evaluated applying a variety of basic digital signal processing tasks ranging from basic filters to complete audio decoders. Estimated power figures for the inspected tasks are compared to physically measured values. A resulting maximum estimation error of less than 8 % is achieved.
Year
DOI
Venue
2006
10.1007/11796435_23
SAMOS
Keywords
Field
DocType
basic flpa approach,basic digital signal processing,power estimation,basic flpa modeling concept,good modeling accuracy,embedded general purpose processor,arithmetic function,power consumption,estimated power figure,embedded processor,basic filter,instruction level power modeling,power analysis,processor architecture,digital signal processing
Signal processing,Power analysis,Clock network,Digital signal processing,Arithmetic function,Degree of parallelism,Computer science,Parallel computing,Energy consumption,Microarchitecture
Conference
Volume
ISSN
ISBN
4017
0302-9743
3-540-36410-2
Citations 
PageRank 
References 
5
0.64
6
Authors
5
Name
Order
Citations
PageRank
Holger Blume122042.84
D. Becker2455.68
Martin Botteck3595.02
Jörg Brakensiek4898.94
Tobias G. Noll519937.51