Analysis of Multi-Objective Evolutionary Algorithms to Optimize Dynamic Data Types in Embedded Systems

New multimedia embedded applications are increasingly dynamic, and rely on Dynamically-allocated Data Types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large design space of possible DDTs implementations. Thus, suitable exploration methods for embedded design metrics (memory accesses, memory usage and power consumption) need to be developed. In this work we present a detailed analysis of the characteristics of different types of Multi-Objective Evolutionary Algorithms (MOEAs) to tackle the optimization of DDTs in multimedia applications and compare them with other state-of-the-art heuristics. Our results with state-of-the-art MOEAs in two object-oriented multimedia embedded applications show that more sophisticated MOEAs (SPEA2 and NSGA-II) offer better solutions than simple schemes (VEGA). Moreover, the suitable sophisticated scheme varies according to the available exploration time, namely, NSGA-II outperforms SPEA2 in the first set of solutions (300-500 generations), while SPEA2 offers better solutions afterwards

Mixed Heuristic and Mathematical Programming Using Reference Points for Dynamic Data Types Optimization in Multimedia Embedded Systems

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)

Parallel and Distributed Optimization of Dynamic Data Structures for Multimedia Embedded Systems

Energy-efficient design of multimedia embedded systems demands optimizations in both hardware and software. Software optimization has no received much attention, although modern multimedia applications exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization is played by the Dynamic Data Types (DDTs) that reside in every reallife application. It would be desirable to organize this set of DDTs to achieve the best performance in the target embedded system. This problem is NP-complete, and cannot be fully explored. In these cases the use of parallel processing can be very useful because it allows not only to explore more solutions spending the same time, but also to implement new algorithms. In this work, we propose a method that uses parallel processing and evolutionary computation to explore DDTs in the design of embedded applications. We propose a parallel Multi-Objective Evolutionary Algorithm (MOEA) which combines NSGA-II and SPEA2. We use Discrete Event Systems Specification (DEVS) to implement this parallel evolutionary algorithm over Service Oriented Architecture (SOA). Parallelism improves the solutions found by the corresponding sequential algorithms, and it allows system designers to reach better solutions than previous approximation

Optimization of Multimedia Embedded Applications using Parallel Genetic Algorithms

Energy-efficient design of multimedia embedded systems demands optimizations in both hardware and software. Software optimization has no received much attention, although modern multimedia applications exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization is played by the Dynamic Data Types (DDTs) that reside in every reallife application. It would be desirable to organize this set of DDTs to achieve the best performance in the target embedded system. This problem is NP-complete, and cannot be fully explored. In these cases the use of parallel processing can be very usefull because it allows not only to explore more solutions spending the same time, but also to implement new algorithms. In this work, we propose a method that uses parallel processing and evolutionary computation to explore DDTs in the design of embedded applications. We propose a parallel Multi-Objective Evolutionary Algorithm (MOEA) which combines NSGA-II and SPEA2. We use Discrete Event Systems Specification (DEVS) to implement this parallel evolutionary algorithm over Service Oriented Architecture (SOA). Parallelism improves the solutions found by the corresponding sequential algorithms, and it allows system designers to reach better solutions than previous approximation