Instruction level power analysis and optimization of software
Journal of VLSI Signal Processing Systems - Special issue on technologies for wireless computing
Function-level power estimation methodology for microprocessors
Proceedings of the 37th Annual Design Automation Conference
Wattch: a framework for architectural-level power analysis and optimizations
Proceedings of the 27th annual international symposium on Computer architecture
ARM System-on-Chip Architecture
ARM System-on-Chip Architecture
Power Consumption Estimation of a C Program for Data-Intensive Applications
PATMOS '02 Proceedings of the 12th International Workshop on Integrated Circuit Design. Power and Timing Modeling, Optimization and Simulation
Journal of Systems Architecture: the EUROMICRO Journal
Application Domain Specific Embedded FPGAs for Flexible ISA-Extension of ASIPs
Journal of Signal Processing Systems
Hi-index | 0.00 |
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.