Timing Analysis on Code-Level (TACLe)
|Name||Timing Analysis on Code-Level|
|Role of TUHH||Action Vice Chair, member of Working Groups 1, 2 and 4|
|Funds Donor||COST Office Brussels|
TACLe is a four years lasting COST Action funded by the COST Office in Brussels.
Many embedded systems are safety-critical real-time systems that must process data within given deadlines. To validate real-time properties, timing analyses of program code are mandatory. Research on techniques for timing analysis of software touches many areas within computer science, e.g., computer architecture, compiler construction and formal verification.
This COST Action aims to cross-link the leading European researchers in these areas and thus to strengthen Europe's leading position in the field of timing analysis. TACLe's research activities include timing models for multicore systems, support of timing analysis by software development tools, early-stage timing analysis right in the beginning of the software development cycle, and the consideration of resources other than time like, e.g., energy dissipation.
TACLe Publications of the Embedded Systems Design Group
|Title: Code Optimization of Periodic Preemptive Hard Real-Time Multitasking Systems. <em>In Proceedings of the 18th International Symposium on Real-Time Distributed Computing (ISORC)</em>|
|Written by: Arno Luppold and Heiko Falk|
|in: April (2015).|
|on pages: 35-42|
|Address: Auckland / New Zealand|
|how published: 15-80 LuFa15a ISORC|
Note: aluppold, hfalk, ESD, emp2, tacle, WCC
Abstract: In hard real-time systems, each task has to provably finish its execution within its respective deadline. Compiler optimizations can be used to improve each task's timing behavior. However, current compilers do not consider tasks' deadlines and can therefore not be used to reliably optimize hard real-time systems with regard to its schedulability. We propose a compiler optimization framework based on Integer-Linear Programming which allows for schedulability aware code optimizations of hard real-time multitasking systems. We evaluate the framework using an instruction scratchpad optimization. The results show that our approach can be used to improve the schedulability of hard real-time systems significantly.