Tutorial: Evolutionary Design Methods in Electronic Design Automation

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@InProceedings{Sekanina:2024:ICCD,

• author = "Lukas Sekanina",
• title = "Tutorial: Evolutionary Design Methods in Electronic Design Automation",
• booktitle = "2024 IEEE 42nd International Conference on Computer Design (ICCD)",
• year = "2024",
• pages = "689--690",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Surveys, Filters, Scalability, Design methodology, Neural networks, Tutorials, Performance gain, Integrated circuit modelling, Formal verification, accelerator, approximate circuit, electronic design automation, machine learning",
• ISSN = "2576-6996",
• DOI = "doi:10.1109/ICCD63220.2024.00110",
• abstract = "The use of evolutionary algorithms (EAs) for the automated design of programs, electronic circuits, neural networks, and other computational structures has become a fruitful approach in the last two decades. The advantage of EAs is that they can handle the design process in a holistic, multi-objective way and create solutions with unique properties. This tutorial surveys the key ingredients of EAs and focuses mainly on genetic programming. It presents several techniques (such as incorporating formal verification methods and surrogate models) to improve the scalability of the method. Examples of evolved solutions (approximate arithmetic circuits, neural network architectures, and image filters) that show unique properties compared to conventional designs are presented and discussed.",
• notes = "Also known as \cite{10818214}",

}

Genetic Programming entries for Lukas Sekanina

Citations