Michael Glaß

Opt4J: a modular framework for meta-heuristic optimization

This paper presents a modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately. Since these subtasks are generally correlated, a separate optimization is prohibited and the framework has to be capable of optimizing the subtasks concurrently. For this purpose, a distinction of genetic representation (genotype) and representation of a solution of the optimization problem (phenotype) is imposed. A compositional genotype and appropriate operators enable the separate development and testing of the optimization of subtasks by a strict decoupling. The proposed concept is implemented as open source reference OPT4J [6]. The architecture of this implementation is outlined and design decisions are discussed that enable a maximal decoupling and flexibility. A case study of a complex real-world optimization problem from the automotive domain is introduced. This case study requires the concurrent optimization of several heterogeneous aspects. Exemplary, it is shown how the proposed framework allows to efficiently optimize this complex problem by decomposing it into subtasks that are optimized concurrently.

FlexRay schedule optimization of the static segment

The FlexRay bus is the prospective automotive standard communication system. For the sake of a high exibility, the protocol includes a static time-triggered and a dynamic event-triggered segment. This paper is dedicated to the scheduling of the static segment in compliance with the automotive-specific AUTOSAR standard. For the determination of an optimal schedule in terms of the number of used slots, a fast greedy heuristic as well as a complete approach based on Integer Linear Programming are presented. For this purpose, a scheme for the transformation of the scheduling problem into a bin packing problem is proposed. Moreover, a metric and optimization method for the extensibility of partially used slots is introduced. Finally, the provided experimental results give evidence of the benefits of the proposed methods. On a realistic case study, the proposed methods are capable of obtaining better results in a significantly smaller amount of time compared to a commercial tool. Additionally, the experimental results provide a case study on incremental scheduling, a scalability analysis, an exploration use case, and an additional test case to emphasis the robustness and exibility of the proposed methods.

Designing heterogeneous ECU networks via compact architecture encoding and hybrid timing analysis

In this paper, a design method for automotive architectures is proposed. The two main technical contributions are (i) a novel hardware/ software architecture encoding that unifies a number of design steps, i.e., resource allocation, process binding, message routing, scheduling, and parameter estimation for the processor and bus schedulers, and (ii) a hybrid scheme that allows different timing analysis techniques to be applied to different bus protocols (viz., CAN and FlexRay) within the same architecture in order to derive global performance estimates such as end-to-end delays of messages. The use of the compact encoding technique substantially reduces the underlying search space, and the hybrid timing analysis scheme allows the combination of known timing analysis techniques from the real-time systems domain. The proposed techniques were combined into a tool-chain and a real-life case study to illustrate their advantages.

A new time-independent reliability importance measure

Modern digital systems pose new challenges to reliability analysts. Systems may exhibit a non-coherent behavior and time becomes an important element of the analysis due to aging effects. Measuring the importance of system components in a computationally efficient way becomes essential in system design. Herein, we propose a new importance measure for time-independent reliability analysis. The importance measure is based on the change in mean time to failure caused by the failure (success) of a component. It possesses some attractive properties: i) it is defined for both coherent and non-coherent systems; ii) it has an intuitive probabilistic and also geometric interpretation; iii) it is simple to evaluate. It turns out that the proposed importance measure leads naturally to a test of time consistency. We illustrate the properties with examples of coherent and non-coherent systems. A comparison with the ranking of other time-dependent and time-independent reliability importance measures is also offered. The realistic application to the reliability analysis of an H.264 video decoder concludes the work.

Timing analysis of Ethernet AVB-based automotive E/E architectures

Due to ever-increasing bandwidth requirements of modern automotive applications, Ethernet AVB is becoming a standard high-speed bus in automotive E/E architectures. Since Ethernet AVB is tailored to audio and video entertainment, existing analysis approaches neglect the specific requirements and features of heterogeneous E/E architectures and their applications. This paper presents a timing analysis technique based on Real Time Calculus to consider Ethernet AVB in complex E/E architectures, reflecting key features such as static routing and stream reservation, fixed topology, and real-time applications. A comparison with a simulation on case studies from the automotive domain gives evidence that the proposed technique delivers valuable bounds for complete sensor-to-actuator chains, enabling automatic system synthesis and design space exploration approaches.

Multi-objective distributed run-time resource management for many-cores

Dynamic usage scenarios of many-core systems require sophisticated run-time resource management that can deal with multiple often conflicting application and system objectives. This paper proposes an approach based on nonlinear programming techniques that is able to trade off between objectives while respecting targets regarding their values. We propose a distributed application embedding for dealing with soft system-wide constraints as well as a centralized one for strict constraints. The experiments show that both approaches may significantly outperform related heuristics.

Resilience Articulation Point (RAP): Cross-layer dependability modeling for nanometer system-on-chip resilience

Abstract The Resilience Articulation Point (RAP) model aims at provisioning researchers and developers with a probabilistic fault abstraction and error propagation framework covering all hardware/software layers of a System on Chip. RAP assumes that physically induced faults at the technology or CMOS device layer will eventually manifest themselves as a single or multiple bit flip(s). When probabilistic error functions for specific fault origins are known at the bit or signal level, knowledge about the unit of design and its environment allow the transformation of the bit-related error functions into characteristic higher layer representations, such as error functions for data words, Finite State Machine (FSM) state, macro-interfaces or software variables. Thus, design concerns at higher abstraction layers can be investigated without the necessity to further consider the full details of lower levels of design. This paper introduces the ideas of RAP based on examples of radiation induced soft errors in SRAM cells, voltage variations and sequential CMOS logic. It shows by example how probabilistic bit flips are systematically abstracted and propagated towards higher abstraction levels up to the application software layer, and how RAP can be used to parameterize architecture-level resilience methods.

Formal analysis of the startup delay of SOME/IP service discovery

An automotive network needs to start up within the millisecond range. This includes the physical startup, the software boot time, and the configuration of the network. The introduction of Ethernet into the automotive industry expanded the design space drastically and is increasing the complexity of configuring every element in the network. To add more flexibility to automotive Ethernet networks, the concept of Service Discovery was migrated from consumer electronics to AUTOSAR within the SOME/IP middleware. A network is not fully functional until every client has found its service. Consequently, this time interval adds to the startup time of a network. This work presents a formal analysis model to calculate the waiting time of every client to receive the first offer from its service. The model is able to determine the worst case of a given parameter set. Based on this, a method for calculating the total startup time of a system is derived. The model is implemented in a free-to-use octave program and validated by comparing the analytical results to a timing-accurate simulation and an experimental setup. In every case the worst-case assumption holds true - the gap between the maximum of the simulation and the presented method is less than 1.3%.

Stress-Aware Module Placement on Reconfigurable Devices

A lot of research has been spent on improving the reliability and extending the lifetime of ASIC and SoC devices, but only little on improving the long-term reliability of dynamically reconfigurable systems. In order to increase the lifetime of a reconfigurable device, we propose a placement strategy to distribute the stress equally on the reconfigurable resources at runtime such that all have a similar level of degradation. Thereby, we present a new aging model which is applied to estimate the influence of aging effects on dynamically reconfigurable devices, and which can be evaluated at runtime, while providing quite accurate aging results. Furthermore, we present a new stress-aware placement algorithm that takes the degradation of the reconfigurable resources into account and can significantly extend the lifetime of reconfigurable devices.

Solving multi-objective pseudo-boolean problems

Integer Linear Programs are widely used in areas such as routing problems, scheduling analysis and optimization, logic synthesis, and partitioning problems. As many of these problems have a Boolean nature, i.e., the variables are restricted to 0 and 1, so called Pseudo-Boolean solvers have been proposed. They are mostly based on SAT solvers which took continuous improvements over the past years. However, Pseudo-Boolean solvers are only able to optimize a single linear function while fulfilling several constraints. Unfortunately many real-world optimization problems have multiple objective functions which are often conflicting and have to be optimized simultaneously, resulting in general in a set of optimal solutions. As a consequence, a single-objective Pseudo-Boolean solver will not be able to find this set of optimal solutions. As a remedy, we propose three different algorithms for solving multi-objective Pseudo-Boolean problems. Our experimental results will show the applicability of these algorithms on the basis of several test cases.