T-CREST: Time-predictable multi-core architecture for embedded systems
T-CREST: Time-predictable multi-core architecture for embedded systems
复制标题
DOI：
10.1016/j.sysarc.2015.04.002
复制
发表时间：
2015-10-01
影响因子：
4.5
通讯作者：
Tocchi, Alessandro
中科院分区：
文献类型：
--
作者：
Schoeberl, Martin;Abbaspour, Sahar;Tocchi, Alessandro
实时系统需要时间可预测的平台，以允许对最坏情况执行时间（WCET）进行静态分析。标准的多核处理器针对一般情况进行了优化，并且很难进行分析。在T-CREST项目中，我们为时间可预测的多核架构提出了新的解决方案，这些解决方案针对WCET而不是平均执行时间进行了优化。由此产生的时间可预测的资源（处理器，互连，内存仲裁器，内存控制器）和工具（编译器，WCET分析）的目的是简化WCET分析和优化WCET性能。与其他处理器相比，WCET的性能非常出色。T-CREST平台通过两个工业用例进行了评估。航空电子领域的应用程序表明，在不同的核心上执行的任务不会干扰他们的WCET。来自铁路领域的信号处理应用表明，当将任务分布在多个核上并使用片上网络进行通信时，可以减少计算密集型任务的WCET。三个核心的WCET提高了1.8倍，15个核心的WCET提高了5.7倍。T-CREST项目是由来自学术界和工业界的八个合作伙伴执行的合作研发项目的结果。欧盟委员会资助了T-CREST。(C)2015爱思唯尔B. V.保留所有权利。
Real-time systems need time-predictable platforms to allow static analysis of the worst-case execution time (WCET). Standard multi-core processors are optimized for the average case and are hardly analyzable. Within the T-CREST project we propose novel solutions for time-predictable multi-core architectures that are optimized for the WCET instead of the average-case execution time. The resulting time-predictable resources (processors, interconnect, memory arbiter, and memory controller) and tools (compiler, WCET analysis) are designed to ease WCET analysis and to optimize WCET performance. Compared to other processors the WCET performance is outstanding.The T-CREST platform is evaluated with two industrial use cases. An application from the avionic domain demonstrates that tasks executing on different cores do not interfere with respect to their WCET. A signal processing application from the railway domain shows that the WCET can be reduced for computation-intensive tasks when distributing the tasks on several cores and using the network-on-chip for communication. With three cores the WCET is improved by a factor of 1.8 and with 15 cores by a factor of 5.7.The T-CREST project is the result of a collaborative research and development project executed by eight partners from academia and industry. The European Commission funded T-CREST. (C) 2015 Elsevier B.V. All rights reserved.