Optimizing L1 cache for embedded systems through grammatical evolution

Nowadays, embedded systems are provided with cache memories that are large enough to influence in both performance and energy consumption as never occurred before in this kind of systems. In addition, the cache memory system has been identified as a component that improves those metrics by adapting...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2016-06, Vol.20 (6), p.2451-2465
Main Authors: Díaz Álvarez, Josefa, Colmenar, J. Manuel, Risco-Martín, José L., Lanchares, Juan, Garnica, Oscar
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational Intelligence
Configurations
Control
Design
Embedded systems
Energy consumption
Engineering
Evolution
Heuristic methods
Market positioning
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Multimedia
Optimization
Optimization techniques
Reduction
Robotics
Smartphones
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Summary:Nowadays, embedded systems are provided with cache memories that are large enough to influence in both performance and energy consumption as never occurred before in this kind of systems. In addition, the cache memory system has been identified as a component that improves those metrics by adapting its configuration according to the memory access patterns of the applications being run. However, given that cache memories have many parameters which may be set to a high number of different values, designers are faced with a wide and time-consuming exploration space. In this paper, we propose an optimization framework based on Grammatical Evolution (GE) which is able to efficiently find the best cache configurations for a given set of benchmark applications. This metaheuristic allows an important reduction of the optimization runtime obtaining good results in a low number of generations. Besides, this reduction is also increased due to the efficient storage of evaluated caches. Moreover, we selected GE because the plasticity of the grammar eases the creation of phenotypes that form the call to the cache simulator required for the evaluation of the different configurations. Experimental results for the Mediabench suite show that our proposal is able to find cache configurations that obtain an average improvement of 62 % versus a real world baseline configuration.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1653-1