Runtime Precomputation of Data-Dependent Parameters in Embedded Systems

Abstract

In many modern embedded systems, the available resources (e.g., CPU clock cycles, memory, and energy) are consumed nonuniformly while the system is under exploitation. Typically, the resource requirements in the system change with different input data that the system process. These data trigger different parts of the embedded software, resulting in different operations executed that require different hardware platform resources to be used. A significant research effort has been dedicated to develop mechanisms for runtime resource management (e.g., branch prediction for pipelined processors, prefetching of data from main memory to cache, and scenario-based design methodologies). All these techniques rely on the availability of information at runtime about upcoming changes in resource requirements. In this article, we propose a method for detecting upcoming resource changes based on preliminary calculation of software variables that have the most dynamic impact on resource requirements in the system. We apply the method on a modified real-life biomedical algorithm with real input data and estimate a 40% energy reduction as compared to static DVFS scheduling. Comparing to dynamic DVFS scheduling, an 18% energy reduction is demonstrated.