On learning-based methods for design-space exploration with high-level synthesis

This paper makes several contributions to address the challenge of supervising HLS tools for design space exploration (DSE). We present a study on the application of learning-based methods for the DSE problem, and propose a learning model for HLS that is superior to the best models described in the...

Full description

Saved in:
Bibliographic Details
Published in2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors Liu, Hung-Yi, Carloni, Luca P.
Format Conference Proceeding
LanguageEnglish
Published New York, NY, USA ACM 29.05.2013
IEEE
SeriesACM Conferences
Subjects
Approximation methods
Discrete Fourier transforms
Ground penetrating radar
Hardware > Integrated circuits > Logic circuits
High-Level Synthesis
Prediction algorithms
Radio frequency
Solid modeling
System-Level Design
Training
high-level synthesis
system-level design
Online AccessGet full text

Cover

More Information
Summary:This paper makes several contributions to address the challenge of supervising HLS tools for design space exploration (DSE). We present a study on the application of learning-based methods for the DSE problem, and propose a learning model for HLS that is superior to the best models described in the literature. In order to speedup the convergence of the DSE process, we leverage transductive experimental design, a technique that we introduce for the first time to the CAD community. Finally, we consider a practical variant of the DSE problem, and present a solution based on randomized selection with strong theory guarantee.
ISBN:1450320716
9781450320719
ISSN:0738-100X
DOI:10.1145/2463209.2488795