Prospector: Synthesizing Efficient Accelerators via Statistical Learning

Conference Paper

Accelerator design is expensive due to the effort required to understand an algorithm and optimize the design. Architects have embraced two technologies to reduce costs. High-level synthesis automatically generates hardware from code. Reconfigurable fabrics instantiate accelerators while avoiding fabrication costs for custom circuits. We further reduce design effort with statistical learning. We build an automated framework, called Prospector, that uses Bayesian techniques to optimize synthesis directives, reducing execution latency and resource usage in field-programmable gate arrays. We show in a certain amount of time designs discovered by Prospector are closer to Pareto-efficient designs compared to prior approaches.

Full Text

Duke Authors

Cited Authors

  • Mehrabi, A; Manocha, A; Lee, BC; Sorin, DJ

Published Date

  • March 1, 2020

Published In

  • Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, Date 2020

Start / End Page

  • 151 - 156

International Standard Book Number 13 (ISBN-13)

  • 9783981926347

Digital Object Identifier (DOI)

  • 10.23919/DATE48585.2020.9116473

Citation Source

  • Scopus