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: Memory-Aware Optimization of Embedded Software for Multiple Objectives. <em>Handbook of Hardware/Software Codesign</em>
|Written by: Peter Marwedel, Heiko Falk, and Olaf Neugebauer
|in: June (2017).
|Editor: In S. Ha and J. Teich (Eds.)
|how published: 17-85 MFN17 Springer
Note: hfalk, ESD, multiopt, WCC
Abstract: Information processing in Cyber-Physical Systems (CPSs) has to respect a variety of constraints and objectives such as response and execution time, energy consumption, Quality of Service (QoS), size and cost. Due to the large impact of the size of memories on their energy consumption and access times, an exploitation of memory characteristics offers a large potential for optimizations. In this chapter, we will describe optimization approaches proposed by our research groups. We will start with optimizations for single objectives, such as energy consumption and execution time. As a consequence of considering hard real-time systems, special attention is on the minimization of the Worst-Case Execution Time (WCET) within compilers. Three WCET reduction techniques are analyzed: exploitation of scratchpads, instruction cache locking, and cache partitioning for multi-task systems. The last section presents an approach for considering trade-offs between multiple objectives in the design of a cyber-physical sensor system for the detection of bio-viruses.