Automated Circuit Approximation Method Driven by Data Distribution

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Vasicek:2019:DATE,

• author = "Zdenek Vasicek and Vojtech Mrazek and Lukas Sekanina",
• title = "Automated Circuit Approximation Method Driven by Data Distribution",
• booktitle = "2019 Design, Automation Test in Europe Conference Exhibition (DATE)",
• year = "2019",
• editor = "Juergen Teich and Franco Fummi",
• pages = "96--101",
• address = "Florence",
• month = "25-29 " # mar,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-9819263-2-3",
• ISSN = "1530-1591",
• DOI = "doi:10.23919/DATE.2019.8714977",
• size = "6 pages",
• abstract = "We propose an application-tailored data-driven fully automated method for functional approximation of combinational circuits. We demonstrate how an application-level error metric such as the classification accuracy can be translated to a component-level error metric needed for an efficient and fast search in the space of approximate low-level components that are used in the application. This is possible by employing a weighted mean error distance (WMED) metric for steering the circuit approximation process which is conducted by means of genetic programming. WMED introduces a set of weights (calculated from the data distribution measured on a selected signal in a given application) determining the importance of each input vector for the approximation process. The method is evaluated using synthetic benchmarks and application-specific approximate MAC (multiply-and-accumulate) units that are designed to provide the best tradeoff between the classification accuracy and power consumption of two image classifiers based on neural networks.",
• notes = "also known as \cite{8714977}",

}

Genetic Programming entries for Zdenek Vasicek Vojtech Mrazek Lukas Sekanina

Citations