# Multi-Criterial Code Optimization for Embedded Hard Real-Time Systems (Multi-Opt)

### Fact Sheet

Acronym | 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 |

Start Date | 01/10/2017 |

End Date | 30/06/2022 |

Funds Donor | Deutsche Forschungsgemeinschaft (DFG) |

### Summary

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.

### Multi-Opt Publications of the Embedded Systems Design Group

[176843] |

Title: Multi-Objective Optimization for the Compiler of Hard Real-Time Systems. <em>In Proceedings of the 23rd International Symposium on Mathematical Programming (ISMP)</em> |

Written by: Kateryna Muts, Arno Luppold and Heiko Falk |

in: July (2018). |

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Address: Bordeaux / France |

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how published: 18-70 MLF18b ISMP |

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**Note: **kmuts, aluppold, hfalk, multiopt, ESD, WCC

**Abstract: **With the growing complexity of embedded systems software, high code quality can only be achieved using a compiler. Sophisticated compilers provide various optimizations to improve code aggressively w.r.t. different objective functions, e.g., worst-case execution time (WCET) and code size, which usually contradict each other. In order to find a suitable trade-off between these objectives, evolutionary multi-objective algorithms identifying Pareto optimal solutions are exploited. The thirs version of the Generalized Differential Evolution (GDE3) is currently one of the most suitable multi-objective evolutionary algorithms. However, the standard GDE3 cannot be used for solving the binary-coded optimization problems directly. A binary generalized differential evolution algorithm (BGDE) is inspired by the novel modified binary differential evolution algorithm (NMBDE) for single-objective optimization problems and GDE3. The formulas of the standard GDE3, including the mutation, crossover and selection operators, are reserved in BGDE. The probability estimation operator proposed in NMBDE is used to map real-coded vectors, generated by GDE3, to binary-coded vectors. The BGDE and multi-objective binary probability optimization algorithm (MBPOA), which is developed for binary-coded problems, are implemented in a compiler framework for hard real-time systems to perform an automatic minimization of the code size and WCET for the well-known compiler optimization function inlining. A comparison is performed the helps to determine the most suitable multi-objective optimizer.