Modeling and Optimization Frameworks for Runtime Adaptable Embedded Systems

Abstract

The widespread adoption of embedded computing systems has resulted in the realization of numerous sensing, decision, and control applications with diverse application-specific requirements. However, such embedded systems applications are becoming increasingly difficult to design, simulate, and optimize due to the multitude of interdependent parameters that must be considered to achieve optimal, or near-optimal, performance that meets design constraints. This situation is further exacerbated for data-adaptable embedded systems (DAES) applications due to the dynamic characteristics of the deployment environment and the data streams on which these systems operate. As operating conditions change, these embedded systems must continue to adapt their configuration and composition at runtime in order to meet application requirements. To assist both platform developers and application domain experts, this dissertation presents design and optimization frameworks for the synthesis of runtime adaptable embedded systems. For sensor network applications, we present an initial dynamic profiling and optimization platform that profiles network and sensor node activity to generate optimal node configurations at runtime based on designed-specified application requirements. To support a broader class of DAES applications, we present a modeling and optimization framework that supports the specification of application task flows, data types, and runtime estimation models for the runtime adaptation of task implementations and device mappings. Experimental results for these design and optimization frameworks demonstrate the benefits of dynamic optimization compared to static optimization alternatives. For the presented sensor network and video-based collision avoidance applications, dynamic configurations exhibited improvements of up to 109% and 76%, respectively. Moreover, the performance of the heuristic design space exploration (DSE) algorithms utilized by the runtime optimization frameworks is compared to exhaustive DSE implementations, resulting in speedups of up to 1662X and 544X for the same two applications, respectively.