Title :
On learning-based methods for design-space exploration with High-Level Synthesis
Author :
Hung-Yi Liu ; Carloni, Luca P.
Author_Institution :
Dept. of Comput. Sci., Columbia Univ., New York, NY, USA
Abstract :
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.
Keywords :
CAD; design of experiments; high level synthesis; learning systems; CAD community; DSE; HLS tools; design space exploration; high level synthesis; learning based methods; randomized selection; transductive experimental design; Approximation methods; Discrete Fourier transforms; Ground penetrating radar; Prediction algorithms; Radio frequency; Solid modeling; Training; High-Level Synthesis; System-Level Design;
Conference_Titel :
Design Automation Conference (DAC), 2013 50th ACM/EDAC/IEEE
