Composing high-performance memory allocators
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Introduction to the Theory of Computation
Introduction to the Theory of Computation
Dynamic Storage Allocation: A Survey and Critical Review
IWMM '95 Proceedings of the International Workshop on Memory Management
Systematic dynamic memory management design methodology for reduced memory footprint
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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
GEVA: grammatical evolution in Java
ACM SIGEVOlution
Optimization of dynamic memory managers for embedded systems using grammatical evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Efficient system-level prototyping of power-aware dynamic memory managers for embedded systems
Integration, the VLSI Journal - Special issue: Low-power design techniques
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
IEEE Transactions on Evolutionary Computation
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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Technology scaling has offered advantages to embedded systems, such as increased performance, more available memory and reduced energy consumption. However, scaling also brings a number of problems like reliability degradation mechanisms. The intensive activity of devices and high operating temperatures are key factors for reliability degradation in latest technology nodes. Focusing on embedded systems, the memory is prone to suffer reliability problems due to the intensive use of dynamic memory on wireless and multimedia applications. In this work we present a new approach to automatically design dynamic memory managers considering reliability, and improving performance, memory footprint and energy consumption. Our approach, based on Grammatical Evolution, obtains a maximum improvement of 39% in execution time, 38% in memory usage and 50% in energy consumption over state-of-the-art dynamic memory managers for several real-life applications. In addition, the resulting distributions of memory accesses improve reliability. To the best of our knowledge, this is the first proposal for automatic dynamic memory manager design that considers reliability.