Data and memory optimization techniques for embedded systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
SCOPES '07 Proceedingsof the 10th international workshop on Software & compilers for embedded systems
Integrating user preferences with particle swarms for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Memory-access-aware data structure transformations for embedded software with dynamic data accesses
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on the 2002 international symposium on low-power electronics and design (ISLPED)
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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New multimedia embedded applications are becoming increasingly dynamic. Thus, they cannot only rely on static data allocation, and must employ Dynamically-allocated Data Types (DDTs) to store their data and efficiently use the limited physical resources of embedded devices. However, the optimization of the DDTs for each target embedded system is a very time-consuming process due to the large design space of possible DDTs implementations and selection for the memory hierarchy of each specific embedded device. Thus, new suitable exploration methods for embedded design metrics (memory accesses, usage and power consumption) need to be developed. In this paper we analyze the benefits of two different exploration techniques for DDTs optimization: Multi-Objective Particle Swarm Optimization (MOPSO) and a Mixed Integer Linear Program (MILP). Furthermore, we propose a novel MOPSO exploration method, OMOPSO*, which uses MILP solutions, as reference points, to guide a MOPSO exploration and reach solutions closer to the real Pareto front of solutions. Our experiments with two real-life embedded applications show that our algorithm achieves 40% better coverage and set of solutions than state-of-the-art optimization methods for DDTs (MOGAs and other MOPSOs).