Dave: Deriving automatically verilog from English

Hammond Pearce, Benjamin Tan, Ramesh Karri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Specifications for digital systems are provided in natural language, and engineers undertake significant efforts to translate these into the programming languages understood by compilers for digital systems. Automating this process allows designers to work with the language in which they are most comfortable - the original natural language - and focus instead on other downstream design challenges. We explore the use of state-of-the-art machine learning (ML) to automatically derive Verilog snippets from English via fine-tuning GPT-2, a natural language ML system. We describe our approach for producing a suitable dataset of novice-level digital design tasks and provide a detailed exploration of GPT-2, finding encouraging translation performance across our task sets (94.8 % correct), with the ability to handle both simple and abstract design tasks.

Original languageEnglish (US)
Title of host publicationMLCAD 2020 - Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD
PublisherAssociation for Computing Machinery, Inc
Pages27-32
Number of pages6
ISBN (Electronic)9781450375191
DOIs
StatePublished - Nov 16 2020
Event2nd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2020 - Virtual, Online, Iceland
Duration: Nov 16 2020Nov 20 2020

Publication series

NameMLCAD 2020 - Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD

Conference

Conference2nd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2020
CountryIceland
CityVirtual, Online
Period11/16/2011/20/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design

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