Optimizing a combined WCET-WCEC problem in instruction fetching for real-time systems

In real-time systems, time is usually so critical that other parameters such as energy consumption are often not even considered. However, optimizing the worst energy consumption case can be a key factor in systems with severe power-supply limitations. In this paper we study several memory architectures using combined time and energy optimization models for real-time multitasking systems. Each task is modeled using Lock-MS, a method to optimize the WCET of a task, with an added set of constraints to model in the same way the WCEC (worst case energy consumption). Our tested hardware components focus on instruction fetching, including a lockable cache, a line buffer and a sequential prefetch buffer. We test a variety of instruction fetch alternatives optimizing time and energy consumption. Our results show that the accuracy of the estimation of the number of context switches in the worst case may affect very much the resulting WCEC (up to 8 times in our experiments) and that optimizing the WCEC may provide similar execution times than optimizing the WCET, with up to 5 times less energy consumption. Additionally optimization functions combining WCET and WCEC with different weights show very interesting WCET-WCEC trade-offs. This confirms that methodologies testing such optimizations at design time could be very helpful to provide a precise system set-up