Design Flow of Dynamically-Allocated Data Types in Embedded Applications Based on Elitist Evolutionary Computation Optimization

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. This paper presents a design flow to tackle the optimization of DDTs in multimedia applications. By profiling of the original desktop application and using evolutionary algorithms, the proposed approach is able to find solutions 1584x faster than other state-of-the-art heuristics in an automated way. Moreover, we study the use of elitist Multi-Objective Evolutionary Algorithms (MOEAs) to explore DDT implementations, which offer 75% more optimal solutions to the system designer for the implementation of the final embedded application. To this end, we analyze the quality of the solutions by comparing three MOEAS and other optimization heuristics. Our results in two object-oriented multimedia embedded applications show that elitist MOEAs (NSGA-II and SPEA2) offer better solutions than simple non-elitist schemes (VEGA) and alternative well-known optimization heuristics.