Molecular self-organisation in a developmental model for the evolution of large-scale artificial neural networks

We argue that molecular self-organisation during embryonic development allows evolution to perform highly nonlinear combinatorial optimisation. A structured approach to architectural optimisation of large-scale Artificial Neural Networks using this principle is presented. We also present simulation results demonstrating the evolution of an edge detecting retina using the proposed methodology

Architectural optimisation for microelectronic packaging

International audienceThe aim of this paper is to provide a methodical approach for architectural optimization of power microelectronic devices. Because critical parameters of electronic devices are linked with reliability, architectural optimisation, selection of the geometrical parameters of device and optimization of these parameters by iteration method associated by numerical analysis of reliability have to be achieved. In this way, this paper discusses about a methodical and numerical approach for the optimization of reliability in electronic devices, in particular the influence of geometrical parameters on the device reliability

A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules

Enhancing the EAST-ADL error model with HiP-HOPS semantics

EAST-ADL is a domain-specific modelling language for the engineering of automotive embedded systems. The language has abstractions that enable engineers to capture a variety of information about design in the course of the lifecycle — from requirements to detailed design of hardware and software architectures. The specification of the EAST-ADL language includes an error model extension which documents language structures that allow potential failures of design elements to be specified locally. The effects of these failures are then later assessed in the context of the architecture design. To provide this type of useful assessment, a language and a specification are not enough; a compiler-like tool that can read and operate on a system specification together with its error model is needed. In this paper we integrate the error model of EAST-ADL with the precise semantics of HiP-HOPS — a state-of-the-art tool that enables dependability analysis and optimization of design models. We present the integration concept between EAST-ADL structure and HiP-HOPS error propagation logic and its transformation into the HiP-HOPS model. Source and destination models are represented using the corresponding XML formats. The connection of these two models at tool level enables practical EAST-ADL designs of embedded automotive systems to be analysed in terms of dependability, i.e. safety, reliability and availability. In addition, the information encoded in the error model can be re-used across different contexts of application with the associated benefits for cost reduction, simplification, and rationalisation of dependability assessments in complex engineering designs

The IST project MATRICE on MC-CDMA transmission techniques for future Cellular Systems

This paper presents an overview of the European IST project MATRICE (MC-CDMA Transmission Techniques for Integrated Broadband Cellular Systems, IST-2001-3220), describing its tasks, goals and preliminary achievements. The main focus of the MATRICE project is the definition of a new air-interface for future cellular mobile radio systems based on Multicarrier-CDMA modulation techniques and the study of its key building blocks like receiver algorithms and flexible TX components. The nine European partners participating in this project are CEA-LETI (F), France Telecom (F), Instituto de Telecommonicaçõ (P), Mitsubishi Electric ITE-TCL (F), University of Madrid (E), University of Surrey (UK), STMicroelectronics (CH), INSA-IETR (F) and Nokia (D)

Design guidance using simulation-based Bayesian Belief Networks

In our work, the task of complex computer-based system design optimization involves exploration of a number of possible candidate designs matching the optimisation criteria. However, the process by which the possible candidate designs are generated and rated is fundamental to an optimal outcome. It is dependent upon the set of system characteristics deemed relevant by the designer given the systems requirements. We propose a method which is aimed at providing the designer with guidance based upon description of the possible causal relationships between various system characteristics and qualities. This guidance information is obtained by employing principles of multiparadigm simulation to generate a set of data which is then processed by an algorithm to generate a Bayesian Belief Network representation of causalities present in the source system. Furthermore, we address the issues and tools associated with application of the proposed method by presenting a detailed simulation and network generation effort undertaken as part of a significant industrial case study. © 2008 IEEE