Multi-Criterial Code Optimization for Embedded Hard Real-Time Systems (Multi-Opt)
|Multi-Criterial Code Optimization for Embedded Hard Real-Time Systems
(in German: Multikriterielle Code-Optimierung für Eingebettete Harte Echtzeitsysteme)
|Role of TUHH
|Deutsche Forschungsgemeinschaft (DFG)
Embedded hard real-time systems often have to meet additional design constraints beyond their worst-case timing constraints. Systems operated on battery power have a limited amount energy available and should thus be as energy-efficient as possible. In addition, instruction, data and main memories of typical embedded processor architectures are also frequently severely limited due to technical limitations or given financial budgets. While designing embedded systems, these additional criteria also have to be considered, besides the system's real-time constraints.
In order to achieve a correctly designed system, it has to meet all of the imposed resource constraints. If a system violates one or several design constraints, either the hardware platform must be modified or the resource demand of the software must be lowered. Modifying the hardware usually comes with an increase in costs and hardly predictable side effects. For example, exchanging the system's micro-controller in order to reduce power consumption will lead to changes in temporal behavior. Reducing the resource demand of the software by simply removing parts of the code is also not easily possible without compromising the correct functional behavior of the system.
As a result, this project aims at optimizing embedded software systems at the compiler level with respect to multiple different design requirements. While translating source code to executable code, the compiler will aim to generate optimized code that finally fulfills all constraints with respect to multiple design criteria. However, current compilers are not able to achieve this, because multi-criterial system design is a highly volatile process. The optimization goals interfere with or may even directly contradict each other. Therefore, as part of this proposal, new optimization methods will be researched, implemented end evaluated for existing embedded hardware architectures. We focus on three of the most important criteria that embedded system designers are facing: Worst-Case Execution Time (WCET), code size and energy consumption.
|Title: Predicting Objectives on a Reduced Search Space of Multiobjective Function Inlining. <em>In Proceedings of the 24th International Workshop on Software & Compilers for Embedded Systems (SCOPES)</em>
|Written by: Kateryna Muts and Heiko Falk
|in: November (2021).
|how published: 21-70 MF21b SCOPES
Note: kmuts, hfalk, multiopt, ESD, WCC
Abstract: The Worst-Case Execution Time (WCET), energy consumption, and code size are among the most important criteria of hard real-time systems. To estimate the WCET and energy consumption at compile time, static analyzers are often used: they estimate the objectives by invoking time-consuming microarchitecture, data flow, and control flow analyses. The expensive analyses make it almost infeasible to use evolutionary algorithms for solving multiobjective problems with these two objectives at compile time, since any evolutionary algorithm extensively evaluates objectives to find solutions. We propose a method that speeds up an evolutionary algorithm supplying it with a reduced search space and prediction model fitted on the reduced search space, so the algorithm needs to explore a smaller search space and can use fast predictions instead of time-consuming estimations to evaluate the WCET and energy consumption. The proposed approach is general enough to be used for any compiler-based optimization. We demonstrate the advantages of it solving a multiobjective function inlining problem at compile time.