Authors:
Maha Idrissi Aouad
1
;
René Schott
2
and
Olivier Zendra
1
Affiliations:
1
Grand Est / LORIA, France
;
2
Nancy-Université, Université Henri Poincaré, France
Keyword(s):
Energy consumption reduction, Genetic heuristics, Memory allocation management, Optimizations.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Decision Support Systems
;
Enterprise Software Technologies
;
Intelligent Problem Solving
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
Nowadays, reducing memory energy has become one of the top priorities of many embedded systems designers. Given the power, cost, performance and real-time advantages of Scratch-Pad Memories (SPMs), it is not surprising that SPM is becoming a common form of SRAM in embedded processors today. In this paper, we focus on heuristic methods for SPMs careful management in order to reduce memory energy consumption. We propose Genetic Heuristics for memory management which are, to the best of our knowledge, new original alternatives to the best known existing heuristic (BEH). Our Genetic Heuristics outperform BEH. In fact, experimentations performed on our benchmarks show that our Genetic Heuristics consume from 76.23% up to 98.92% less energy than BEH in different configurations. In addition they are easy to implement and do not require list sorting (contrary to BEH).