TY - JOUR T1 - Automated Model-based Optimization of Data-Adaptable Embedded Systems JF - ACM Transactions on Embedded Computing Systems Y1 - 2019 A1 - Adrian Lizarraga A1 - Jonathan Sprinkle A1 - Roman Lysecky AB -

This paper presents a modeling and optimization framework that enables developers to model an application's data sources, tasks, and exchanged data tokens; specify application requirements through high-level design metrics and fuzzy logic based optimization rules; and define an estimation framework to automatically optimize the application at runtime. We demonstrate the modeling and optimization process via an example application for video-based vehicle tracking and collision avoidance. We analyze the benefits of runtime optimization by comparing the performance of static point solutions to dynamic solutions over five distinct execution scenarios, showing improvements of up to 74% for dynamic over static configurations.

VL - 19 UR - https://doi.org/10.1145/3372142 IS - 1 ER -