Stack Size Minimization for Embedded Real-Time Systems-on-a-Chip


The primary goal for real-time kernel software for single and multiple-processor on a chip systems is to support the design of timely and cost effective systems. The kernel must provide time guarantees, in order to predict the timely behaviorof the application, an extremely fast response time, in order not to waste computing power outside of the application cycles and save as much RAM space as possible in order to reduce the overall cost of the chip. The research on real-time software systems has produced algorithms that allow to effectively schedule system resources while guaranteeing the deadlines of the application and to group tasks in a very small number of non-preemptive sets which require much less RAM memory for stack. Unfortunately, up to now the research focus has been on time guarantees rather than on the optimization of RAM usage.Furthermore, these techniques do not apply to multiprocessor architectures which are likely to be widely used in future microcontrollers. This paper presents innovative scheduling and optimization algorithms that effectively solve the problem of guaranteeing schedulability with an extremely little operating system overhead and minimizing RAM usage. We developed a fast and simple algorithm for sharing resources in multiprocessor systems, together with an innovative procedure for assigning a preemption threshold to tasks. These allow the use of a single user stack. The experimental part shows the effectiveness of a simulated annealing-based tool that allows to find a schedulable system configuration starting from the selection of a near-optimal task allocation. When used in conjunction with our preemption threshold assignment algorithm, our tool further reduces the RAM usage in multiprocessor systems.

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Gai, P., Lipari, G. & Di Natale, M. Stack Size Minimization for Embedded Real-Time Systems-on-a-Chip. Design Automation for Embedded Systems 7, 53–87 (2002).

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  • Multiprocessor scheduling
  • operating systems
  • real-time
  • stack size minimization