Year: 2020
Type: conference-paper
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.
Authors: Hammond Pearce, Benjamin Tan, Ramesh Karri
Institution: New York University
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Office of Naval Research Global
Award: G670020277
OpenAlex ID: W3082750925