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: Real-Time Partitioned Scheduling on Multi-Core Systems with Local and Global Memories. <em>In Proceedings of the 18th Asia and South Pacific Design Automation Conference (ASP-DAC)</em>|
|Written by: Che-Wei Chang, Jian-Jia Chen, Tei-Wei Kuo and Heiko Falk|
|in: January (2013).|
|on pages: 467-472|
|Address: Yokohama / Japan|
|how published: 13-95 CCK+13 ASP-DAC|
Note: hfalk, ESD, emp2, tacle
Abstract: Real-time task scheduling becomes even more challenging with the emerging of island-based multi-core architecture, where the local memory module of an island offers shorter access time than the global memory module does. With such a popular architecture design in mind, this paper exploits real-time task scheduling over island-based homogeneous cores with local and global memory pools. Joint considerations of real-time scheduling and memory allocation are presented to efficiently use the computing and memory resources. A polynomial-time algorithm with an asymptotic 4-approximation bound is proposed to minimize the number of needed islands to successfully schedule tasks. To evaluate the performance of the proposed algorithm, 82 benchmarks from the MRTC, MediaBench, UTDSP, NetBench, and DSPstone benchmark suites were profiled by a worst-case-execution-time analyzer aiT and included in the experiments.