Chip-Chat: Challenges and Opportunities in Conversational Hardware Design

Jason Blocklove, Siddharth Garg, Ramesh Karri, Hammond Pearce

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

Abstract

Modern hardware design starts with specifications provided in natural language. These are then translated by hardware engineers into appropriate Hardware Description Languages (HDLs) such as Verilog before synthesizing circuit elements. Automating this translation could reduce sources of human error from the engineering process. But, it is only recently that artificial intelligence (AI) has demonstrated capabilities for machine-based end-to-end design translations. Commercially-available instruction-tuned Large Language Models (LLMs) such as OpenAI's ChatGPT and Google's Bard claim to be able to produce code in a variety of programming languages; but studies examining them for hardware are still lacking. In this work, we thus explore the challenges faced and opportunities presented when leveraging these recent advances in LLMs for hardware design. Given that these 'conversational' LLMs perform best when used interactively, we perform a case study where a hardware engineer co-architects a novel 8-bit accumulator-based microprocessor architecture with the LLM according to real-world hardware constraints. We then sent the processor to tapeout in a Skywater 130nm shuttle, meaning that this 'Chip-Chat' resulted in what we believe to be the world's first wholly-AI-written HDL for tapeout.

Original languageEnglish (US)
Title of host publication2023 ACM/IEEE 5th Workshop on Machine Learning for CAD, MLCAD 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350309553
DOIs
StatePublished - 2023
Event5th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2023 - Snowbird, United States
Duration: Sep 10 2023Sep 13 2023

Publication series

Name2023 ACM/IEEE 5th Workshop on Machine Learning for CAD, MLCAD 2023

Conference

Conference5th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2023
Country/TerritoryUnited States
CitySnowbird
Period9/10/239/13/23

Keywords

  • CAD
  • Hardware Design
  • LLM

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization
  • Modeling and Simulation

Fingerprint

Dive into the research topics of 'Chip-Chat: Challenges and Opportunities in Conversational Hardware Design'. Together they form a unique fingerprint.

Cite this