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 non-uniformly while the system is under exploitation. Typically, the resource requirements in the system change with different input data that the system process. This 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 run-time 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 run-time about the upcoming changes in the resource requirements. In this paper we propose a method for detection of upcoming resource changes based on preliminary calculation of software variables that have the most dynamic impact on the 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. CCS Concepts: • Computer systems organization → Embedded systems; Firmware; Real-time systems; • Hardware → On-chip resource management;