A design framework for real-time embedded systems with code size and energy constraints

Abstract

Real-time embedded systems are typically constrained in terms of three system performance criteria: space, time, and energy. The performance requirements are directly translated into constraints imposed on the system's resources, such as code size, execution time, and energy consumption. These resource constraints often interact or even conflict with each other in a complex manner, making it difficult for a system developer to apply a well-defined design methodology in developing a real-time embedded system. Motivated by this observation, we propose a design framework that can flexibly balance the tradeoff involving the system's code size, execution time, and energy consumption. Given a system specification and an optimization criteria, the proposed technique generates a set of design parameters in such a way that a system cost function is minimized while the given resource constraints are satisfied. Specifically, the technique derives code generation decision for each task so that a specific version of code is selected among a number of different ones that have distinct characteristics in terms of code size and execution time. In addition, the design framework determines the voltage/frequency setting for a variable voltage processor whose supply voltage can be adjusted at runtime in order to minimize the energy consumption while execution performance is degraded accordingly. The proposed technique formulates this design process as a constrained optimization problem. We show that this optimization problem is NP-hard and then provide a heuristic solution to it. We show that these seemingly conflicting design goals can be pursued by using a simple optimization algorithm that works with a single optimization criteria. Moreover, the optimization is driven by an abstract system specification given by the system developer, so that the system development process can be automated. The results from our simulation show that the proposed algorithm finds a solution that is close to the optimal one with the average error smaller than 1.0%.