Machine Learning for Agile FPGA Design

Debjit Pal (), Chenhui Deng (), Ecenur Ustun (), Cunxi Yu () and Zhiru Zhang ()
Additional contact information
Debjit Pal: Cornell University
Chenhui Deng: Cornell University
Ecenur Ustun: Cornell University
Cunxi Yu: University of Utah
Zhiru Zhang: Cornell University

Chapter Chapter 16 in Machine Learning Applications in Electronic Design Automation, 2022, pp 471-504 from Springer

Abstract: Abstract Field-programmable gate arrays (FPGAs) have become popular means of hardware acceleration since they offer massive parallelism, flexible configurability, and potentially higher performance per Watt. However, the heterogeneous architecture of modern FPGAs and multiple abstractions across design stages present unprecedented challenges to FPGA design tasks, e.g., quality of results (QoR) estimation, and design space exploration, necessitating considerable manual effort for design optimization. Recently, machine learning (ML) has been applied extensively to such FPGA design tasks to minimize human supervision. In this chapter, we provide a comprehensive review of different ML techniques that hold promise to significantly enhance FPGA design automation. First, we provide a brief overview of the FPGA design flow followed by our insights into applying ML for enhanced agility in FPGA design automation. Then, we discuss representative works in applying ML in two different ways for FPGA design automation—ML as a predictor to improve QoR estimation and ML as a decision-maker to automate FPGA design space exploration to iteratively improve QoR estimation. Next, we present multiple recent case studies in detail to showcase the effective applications of ML in FPGA design optimization tasks. Finally, we highlight additional challenges and future opportunities to motivate more ML-based solutions to streamline fast and accurate FPGA design automation.

Keywords: FPGA; Computer-aided design; Design space exploration; Machine learning; Quality of results (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13074-8_16

Ordering information: This item can be ordered from
http://www.springer.com/9783031130748

DOI: 10.1007/978-3-031-13074-8_16

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().