Multi-Opt - Multi-criterial Code Optimization for Embedded Hard Real-Time Systems

硬实时系统的关键要求是满足时间约束。然而，嵌入式系统通常具有由于技术限制或财务预算而不可忽略的额外设计约束。例如，真实的体系结构的程序和数据存储器通常非常紧凑。此外，能耗是电池供电的移动的嵌入式系统的另一个重要标准。因此，在设计硬实时系统时，必须考虑最坏情况执行时间（WCET）、代码大小和能耗这三个关键设计参数，一个设计合理的系统必须满足所有的资源约束。如果违反了一个或多个设计约束，则可以修改硬件平台以满足所有资源约束。调整硬件以实现系统的必要功能，同时满足所有资源需求，通常会导致成本大幅增加。此外，这种硬件修改的副作用难以预测。因此，更可取的方法是通过编译器优化嵌入式系统的软件，试图满足所有设计要求。在第一个资助期内，该项目重点探索基于编译器的优化WCET、代码大小和能耗的方法。在编译时将它们视为优化目标会导致多标准优化，因为它们不能同时完美优化。这些标准通常相互矛盾，即，改善其中一个可能会对其他人产生负面影响。在第一个供资期间，已经表明基于人口的优化算法能够提供良好的结果，成功地交易不同的矛盾目标。然而，由于为了评估这些目标函数而执行所需的时序分析的成本很高，因此这种多标准优化非常耗时和消耗存储器。预测目标函数的值可以改善编译器的运行时间，而不是在每次优化需要目标值时都进行代价高昂的分析。基于这些原因，本文的后续建议旨在开发基于目标值预测的新方法，以支持编译时基于群体的优化。该项目将侧重于设计一个框架，允许预测目标，以减少依赖昂贵分析的多标准优化的运行时间。我们的目标是设计一个基于数学预测模型的框架，允许以有效和高效的方式同时针对多个标准优化嵌入式软件。

Key requirement of hard real-time systems is to meet timing constraints. However, embedded systems often have additional design constraints that are also not negligible due to technical limitations or financial budget. For example, program and data memories of real architectures are often very tight. Moreover, energy consumption is another important criterion for battery-powered, mobile embedded systems. Therefore, three critical design parameters, namely Worst-Case Execution Time (WCET), code size and energy consumption, have to be considered designing hard real-time systems.A properly designed system must comply with all resource constraints. If one or several design constraints are violated, the hardware platform can be modified in order to meet all resource constraints. Adapting hardware to achieve the necessary functionality of the system together with meeting all resource requirements usually leads to greatly increased costs. Furthermore, side effects of such hardware modifications are difficult to predict. Therefore, a more preferable approach is to optimize an embedded system’s software by a compiler trying to meet all design requirements.During the first funding period, the project focused on exploring compiler-based methods for optimizing WCET, code size and energy consumption. Considering them as optimization goals at compile-time leads to a multi-criterial optimization, since they cannot be perfectly optimized simultaneously. These criteria usually contradict each other, i.e., improving one of them can have a negative effect on others. It has been shown during the first funding period that population-based optimization heuristics are able to provide good results that successfully trade the different contradicting objectives. However, such multi-criterial optimizations are very time and memory consuming due to costly executions of required timing analyses in order to evaluate these objective functions. Instead of performing costly analyses each and every time that an optimization needs objectives' values, predicting the values of objective functions could help to improve the compiler's run-times.For these reasons, the present follow-up proposal aims to develop novel approaches based on a prediction of the objectives' values in order to support population-based optimizations at compile-time. The project will focus on the design of a framework allowing to predict objectives in order to decrease the run-times of multi-criterial optimizations relying on costly analyses. The aim is to design a framework based on mathematical prediction models that allows to optimize embedded software for multiple criteria simultaneously in both an effective and efficient manner.