The Experts below are selected from a list of 76638 Experts worldwide ranked by ideXlab platform
Eugen Rigger - One of the best experts on this subject based on the ideXlab platform.
-
Estimating the Impact of Design Automation: The Influence of Knowledge on Potential Estimation
Proceedings of the Design Society: International Conference on Engineering Design, 2019Co-Authors: Eugen Rigger, Kristina Shea, Alexander Lutz, Tino StankovićAbstract:Assessing the impact of Design Automation on Design practice prior to its implementation is difficult and subject to uncertainties. One reason for this is the Designers' lack of knowledge about Design Automation. In this work, an industrial case study focusing on conceptual Design of hydraulic circuits is conducted to assess the impact of the Designers' knowledge on Design Automation potential estimation. In particular, the impact of demonstrating a prototypical implementation of a Design Automation application is investigated as a means to enhance the Designers' knowledge about Design Automation. In this respect, a given set of metrics is rated twice to enable a comparative study: prior to and after introducing the Design Automation prototype. The yielded results show that the knowledge impacts the rating and supports reliability of potential estimation. Further, it is shown that Designers acknowledge Design Automation potential for the early stages of Design given sufficient knowledge about Design Automation. Yet, the results also indicate that careful attention needs to be put on the aspects covered by the prototype in order to avoid biasing participants.
-
Task categorisation for identification of Design Automation opportunities
Journal of Engineering Design, 2018Co-Authors: Eugen Rigger, Kristina Shea, Tino StankovićAbstract:Engineering Design Automation has been an active field of research and application for more than five decades. Despite a multitude of available methods stemming from research fields such as Knowledge-Based Engineering (KBE) and Computational Design Synthesis (CDS) the application context in industry is mostly limited to routine tasks. Here we propose a means to foster the transition of academic methods to industrial practice through a comprehensible and comprehensive Design Automation task categorisation that allows practitioners to grasp the opportunities state-of-the-art Design Automation offers. The categorisation is based on a critical investigation of 77 papers stemming from KBE and CDS, respectively, and is detailed with respect to knowledge needed for complete formalisation of a task, i.e. inputs, outputs and goals, as well as available Automation methods. A thorough discussion indicates the context of the work with current Design practice and provides a consolidation of the research fields KBE and CDS including the derivation of technological requirements for Design Automation task definition by practitioners. Thus, this paper contributes not only by consolidating two research fields but also by presenting technological requirements for further streamlining the process of realising Design Automation applications in industry.
-
Design Automation STATE OF PRACTICE - POTENTIAL AND OPPORTUNITIES
Proceedings of the DESIGN 2018 15th International Design Conference, 2018Co-Authors: Eugen Rigger, Thomas VosgienAbstract:Design Automation has been in focus of research and application for several decades. This paper aims at establishing the current view of Design Automation and identification of potential for adoption based on a survey conducted in German speaking countries and a hypothesis based multivariate analysis based on networks. The findings show that Design Automation is still considered a means of Automation of repetitive Design tasks and potential for enhanced application exists. The necessity for methods supporting Designers for identification and definition of Design Automation tasks is urged.
Loic Lagadec - One of the best experts on this subject based on the ideXlab platform.
-
Model-driven physical-Design Automation for FPGAs: fast prototyping and legacy reuse
Software: Practice and Experience, 2013Co-Authors: Ciprian Teodorov, Loic LagadecAbstract:ReDesign of nontrivial software is often a challenge. Ciprian Teodorov et al. [4] addressed physical-Design Automation and presented a methodological approach relying on model-driven engineering. Also, they summarized some lessons learned from the incremental reDesign of Madeo, a toolkit that targets field-programmable gate array Design Automation.
-
FPGA Physical-Design Automation using Model-Driven Engineering
2011Co-Authors: Ciprian Teodorov, Damien Picard, Loic LagadecAbstract:The problem of physical Design Automation is a difficult problem due to the huge number of devices, and physical constraints to be met. The Model-Driven Engineering (MDE) approach aims to tackle the complexity of software development using a high level method based on models and transformations. While this approach is used for High-Level circuit synthesis there is no work reported on the lower part of the circuit Design Automation flow, namely the physical-Design Automation. In this work, we use the MDE approach to model the physical synthesis process with a focus mainly on reconfigurable architectures. We present a model for island style FPGAs along with the transformations needed in the case of the physical synthesis. The main result of this work is to show the feasibility of the MDE approach for the physical Design Automation problem, and we argue that this methodology enables orthogonal composition of the architecture / algorithms / application Design space, that enables incremental exploration based on quantitative evaluations.
-
ReCoSoC - FPGA physical-Design Automation using Model-Driven Engineering
6th International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC), 2011Co-Authors: Ciprian Teodorov, Damien Picard, Loic LagadecAbstract:The physical Design Automation is a difficult problem due to the huge number of devices, and physical constraints to be met. The Model-Driven Engineering (MDE) approach aims to tackle the complexity of software development using a high level method based on models and transformations. While this approach is used for High-Level circuit synthesis there is no work reported on the lower part of the circuit Design Automation flow, namely the physical-Design Automation. In this work, we use the MDE approach to model the physical synthesis process with a focus mainly on reconfigurable architectures. We present a model for island style FPGAs along with the transformations needed in the case of the physical synthesis. The main result of this work is to show the feasibility of the MDE approach for the physical Design Automation problem, and we argue that this methodology enables orthogonal composition of the architecture / algorithms / application Design space, that enables incremental exploration based on quantitative evaluations.
Tino Stanković - One of the best experts on this subject based on the ideXlab platform.
-
Estimating the Impact of Design Automation: The Influence of Knowledge on Potential Estimation
Proceedings of the Design Society: International Conference on Engineering Design, 2019Co-Authors: Eugen Rigger, Kristina Shea, Alexander Lutz, Tino StankovićAbstract:Assessing the impact of Design Automation on Design practice prior to its implementation is difficult and subject to uncertainties. One reason for this is the Designers' lack of knowledge about Design Automation. In this work, an industrial case study focusing on conceptual Design of hydraulic circuits is conducted to assess the impact of the Designers' knowledge on Design Automation potential estimation. In particular, the impact of demonstrating a prototypical implementation of a Design Automation application is investigated as a means to enhance the Designers' knowledge about Design Automation. In this respect, a given set of metrics is rated twice to enable a comparative study: prior to and after introducing the Design Automation prototype. The yielded results show that the knowledge impacts the rating and supports reliability of potential estimation. Further, it is shown that Designers acknowledge Design Automation potential for the early stages of Design given sufficient knowledge about Design Automation. Yet, the results also indicate that careful attention needs to be put on the aspects covered by the prototype in order to avoid biasing participants.
-
Task categorisation for identification of Design Automation opportunities
Journal of Engineering Design, 2018Co-Authors: Eugen Rigger, Kristina Shea, Tino StankovićAbstract:Engineering Design Automation has been an active field of research and application for more than five decades. Despite a multitude of available methods stemming from research fields such as Knowledge-Based Engineering (KBE) and Computational Design Synthesis (CDS) the application context in industry is mostly limited to routine tasks. Here we propose a means to foster the transition of academic methods to industrial practice through a comprehensible and comprehensive Design Automation task categorisation that allows practitioners to grasp the opportunities state-of-the-art Design Automation offers. The categorisation is based on a critical investigation of 77 papers stemming from KBE and CDS, respectively, and is detailed with respect to knowledge needed for complete formalisation of a task, i.e. inputs, outputs and goals, as well as available Automation methods. A thorough discussion indicates the context of the work with current Design practice and provides a consolidation of the research fields KBE and CDS including the derivation of technological requirements for Design Automation task definition by practitioners. Thus, this paper contributes not only by consolidating two research fields but also by presenting technological requirements for further streamlining the process of realising Design Automation applications in industry.
Ciprian Teodorov - One of the best experts on this subject based on the ideXlab platform.
-
Model-driven physical-Design Automation for FPGAs: fast prototyping and legacy reuse
Software: Practice and Experience, 2013Co-Authors: Ciprian Teodorov, Loic LagadecAbstract:ReDesign of nontrivial software is often a challenge. Ciprian Teodorov et al. [4] addressed physical-Design Automation and presented a methodological approach relying on model-driven engineering. Also, they summarized some lessons learned from the incremental reDesign of Madeo, a toolkit that targets field-programmable gate array Design Automation.
-
FPGA Physical-Design Automation using Model-Driven Engineering
2011Co-Authors: Ciprian Teodorov, Damien Picard, Loic LagadecAbstract:The problem of physical Design Automation is a difficult problem due to the huge number of devices, and physical constraints to be met. The Model-Driven Engineering (MDE) approach aims to tackle the complexity of software development using a high level method based on models and transformations. While this approach is used for High-Level circuit synthesis there is no work reported on the lower part of the circuit Design Automation flow, namely the physical-Design Automation. In this work, we use the MDE approach to model the physical synthesis process with a focus mainly on reconfigurable architectures. We present a model for island style FPGAs along with the transformations needed in the case of the physical synthesis. The main result of this work is to show the feasibility of the MDE approach for the physical Design Automation problem, and we argue that this methodology enables orthogonal composition of the architecture / algorithms / application Design space, that enables incremental exploration based on quantitative evaluations.
-
ReCoSoC - FPGA physical-Design Automation using Model-Driven Engineering
6th International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC), 2011Co-Authors: Ciprian Teodorov, Damien Picard, Loic LagadecAbstract:The physical Design Automation is a difficult problem due to the huge number of devices, and physical constraints to be met. The Model-Driven Engineering (MDE) approach aims to tackle the complexity of software development using a high level method based on models and transformations. While this approach is used for High-Level circuit synthesis there is no work reported on the lower part of the circuit Design Automation flow, namely the physical-Design Automation. In this work, we use the MDE approach to model the physical synthesis process with a focus mainly on reconfigurable architectures. We present a model for island style FPGAs along with the transformations needed in the case of the physical synthesis. The main result of this work is to show the feasibility of the MDE approach for the physical Design Automation problem, and we argue that this methodology enables orthogonal composition of the architecture / algorithms / application Design space, that enables incremental exploration based on quantitative evaluations.
Christopher A. Voigt - One of the best experts on this subject based on the ideXlab platform.
-
Genetic circuit Design Automation for yeast.
Nature microbiology, 2020Co-Authors: Ye Chen, Douglas Densmore, Shuyi Zhang, Eric M. Young, Timothy S. Jones, Christopher A. VoigtAbstract:Cells can be programmed to monitor and react to their environment using genetic circuits. Design Automation software maps a desired circuit function to a DNA sequence, a process that requires units of gene regulation (gates) that are simple to connect and behave predictably. This poses a challenge for eukaryotes due to their complex mechanisms of transcription and translation. To this end, we have developed gates for yeast (Saccharomyces cerevisiae) that are connected using RNA polymerase flux as the signal carrier and are insulated from each other and host regulation. They are based on minimal constitutive promoters (~120 base pairs), for which rules are developed to insert operators for DNA-binding proteins. Using this approach, we constructed nine NOT/NOR gates with nearly identical response functions and 400-fold dynamic range. In circuits, they are transcriptionally insulated from each other by placing ribozymes downstream of terminators to block nuclear export of messenger RNAs resulting from RNA polymerase readthrough. Based on these gates, Cello 2.0 was used to build circuits with up to 11 regulatory proteins. A simple dynamic model predicts the circuit response over days. Genetic circuit Design Automation for eukaryotes simplifies the construction of regulatory networks as part of cellular engineering projects, whether it be to stage processes during bioproduction, serve as environmental sentinels or guide living therapeutics. This study describes Design Automation and predictable gene regulatory network engineering in a eukaryotic microorganism.
