Multi-Criterial Code Optimization for Embedded Hard Real-Time Systems (Multi-Opt)
|Name||Multi-Criterial Code Optimization for Embedded Hard Real-Time Systems|
(in German: Multikriterielle Code-Optimierung für Eingebettete Harte Echtzeitsysteme)
|Role of TUHH||Applicant|
|Funds Donor||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: Compiling for the Worst Case: Memory Allocation for Multi-task and Multi-core Hard Real-time Systems.|
|Written by: Arno Luppold, Dominic Oehlert and Heiko Falk|
|in: <em>ACM Transactions on Embedded Computing Systems (TECS)</em>. March (2020).|
|Volume: <strong>19</strong>. Number: (2),|
|how published: 20-95 LOF20 TECS|
Note: aluppold, doehlert, hfalk, ESD, multiopt, teamplay, WCC
Abstract: Modern embedded hard real-time systems feature multiple tasks running on multiple processing cores. Schedulability analysis of such systems is usually performed on an abstract system level with each task being represented as a black box with fixed timing properties. If timing constraints are violated, optimizing the system on a code-level in order to achieve schedulability is a tedious task. To tackle this issue, we propose an extension to the WCET-Aware C Compiler framework WCC. We integrated an optimization framework based on Integer-Linear Programming into the WCC which is able to optimize a multi-core system with multiple tasks running on each core with regards to its schedulability. We evaluate the framework by providing two approaches on a schedulability aware static Scratchpad Memory (SPM) allocation: One based on Integer-Linear Programming (ILP) and one based on a genetic algorithm.