Observatorio de I+D+i UPM

Descripción
We are currently witnessing an increasing number of energy-bound devices, including in some cases mission critical systems, for which there is a need to optimize their energy consumption and verify that they will perform their function within the available energy budget. In this work we propose a novel parametric approach to estimating tight energy bounds (both upper and lower) that are practical for energy verification and optimization applications in embedded systems. Our approach consists in dividing a program into basic (?branchless?) blocks, establishing the maximal (resp. minimal) energy consumption for each block using an evolutionary algorithm, and combining the obtained values according to the program control flow, using static analysis, to produce energy bound functions. Such functions depend on input data sizes, and return upper or lower bounds on the energy consumption of the program for any given set of input values of those sizes, without running the program. The approach has been tested on XMOS chips, but is general enough to be applied to any microprocessor and programming language. Our experimental results show that the bounds obtained by our prototype tool can be tight while remaining on the safe side of budgets in practice.
Internacional	Si
Nombre congreso	Workshop on High Performance Energy Efficient Embedded Systems (HIP3ES 2016)
Tipo de participación	OTHERS
Lugar del congreso	Praga
Revisores	Si
ISBN o ISSN	arXiv:1601.02800
DOI
Fecha inicio congreso	18/01/2016
Fecha fin congreso	20/01/2016
Desde la página	1
Hasta la página	9
Título de las actas	Proceedings of the Workshop on High Performance Energy Efficient Embedded Systems (HIP3ES 2016)

Esta actividad pertenece a memorias de investigación

• Autor: Umer Liqat (IMDEA Software)
• Autor: Zorana Bankovic (IMDEA Software)
• Autor: Pedro Lopez Garcia (IMDEA Software)
• Autor: Manuel de Hermenegildo Salinas (UPM)

• Creador: Grupo de Investigación: Computación lógica, Lenguajes, Implementación y Paralelismo (CLIP)
• Departamento: Inteligencia Artificial