Automated Exploration of Pareto-optimal Configurations in Parameterized Dynamic Memory Allocation for Embedded Systems
New applications in embedded systems are becoming Increasingly dynamic. In addition to increased dynamism, they have massive data storage needs. Therefore, they rely heavily on dynamic, run-time memory allocation. The design and configuration of a dynamic memory allocation subsystem requires a big design effort, without always achieving the desired results. In this paper, we propose a fully automated exploration of dynamic memory allocation configurations. These configurations are fine tuned to the specific needs of applications with the use of a number of parameters. We assess the effectiveness of the proposed approach in two representative real-life case studies of the multimedia and wireless network domains and show up to 76% decrease in memory accesses and 66% decrease in memory footprint within the Pareto-optimal trade-off space.