Composing high-performance memory allocators
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
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IEEE Transactions on Computers
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Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
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Efficient system-level prototyping of power-aware dynamic memory managers for embedded systems
Integration, the VLSI Journal - Special issue: Low-power design techniques
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Energy-efficient dynamic memory allocators at the middleware level of embedded systems
EMSOFT '06 Proceedings of the 6th ACM & IEEE International conference on Embedded software
Constant creation in grammatical evolution
International Journal of Innovative Computing and Applications
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A parallel evolutionary algorithm to optimize dynamic data types in embedded systems
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An Introduction to Evolutionary Computation in Finance
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Multi-objective optimization of dynamic memory managers using grammatical evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Simulation of high-performance memory allocators
Microprocessors & Microsystems
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New portable consumer embedded devices must execute multimedia applications (e.g., 3D games, video players and signal processing software, etc.) that demand extensive memory accesses and memory usage at a low energy consumption. Moreover, they must heavily rely on Dynamic Memory (DM) due to the unpredictability of the input data and system behavior. Within this context, consistent design methodologies that can tackle efficiently the complex DM behavior of these multimedia applications are in great need. In this article, we present a novel design framework, based on genetic programming, which allows us to design custom DM management mechanisms, optimizing memory accesses, memory use and energy consumption for the target embedded system. First, we describe the large design space of DM management decisions for multimedia embedded applications. Then, we propose a suitable way to traverse this design space using grammatical evolution and construct custom DM managers that minimize the DM used by these highly dynamic applications. As a result, our methodology achieves significant improvements in memory accesses (23% less on average), memory usage (38% less on average) and energy consumption (reductions of 21% on average) in real case studies over the current state-of-the-art DM managers used for these types of dynamic applications. To the best of our knowledge, this is the first approach to efficiently design DM managers for embedded systems using evolutionary computation and grammar evolution.