GUIDE: GenAI Units In Digital Design Education

2026-03-17

AI Summary

This work addresses the lack of structured, reusable, and generative AI–integrated resources in digital design education, which hinders instructional consistency and assessment efficiency. To bridge this gap, we propose GUIDE—a modular, open courseware repository that pioneers the deep integration of large language models (LLMs) into hardware education. GUIDE provides standardized, composable units—each comprising slides, short videos, executable Colab notebooks, and curated papers—to enable flexible assembly of full courses. We also introduce supporting tools such as VeriThoughts and LLMPirate, which automate RTL generation, testbench construction, and IP theft analysis. Four complete courses have been developed using this framework, successfully supporting hands-on projects including hardware Trojan detection. The approach’s efficacy and scalability were further validated through the NYU Cognichip Hackathon, which attracted 24 international teams.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Computer Vision: Computational Photography, Image & Video Synthesis

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

GenAI Units In Digital Design Education (GUIDE) is an open courseware repository with runnable Google Colab labs and other materials. We describe the repository's architecture and educational approach based on standardized teaching units comprising slides, short videos, runnable labs, and related papers. This organization enables consistency for both the students' learning experience and the reuse and grading by instructors. We demonstrate GUIDE in practice with three representative units: VeriThoughts for reasoning and formal-verification-backed RTL generation, enhanced LLM-aided testbench generation, and LLMPirate for IP Piracy. We also provide details for four example course instances (GUIDE4ChipDesign, Build your ASIC, GUIDE4HardwareSecurity, and Hardware Design) that assemble GUIDE units into full semester offerings, learning outcomes, and capstone projects, all based on proven materials. For example, the GUIDE4HardwareSecurity course includes a project on LLM-aided hardware Trojan insertion that has been successfully deployed in the classroom and in Cybersecurity Games and Conference (CSAW), a student competition and academic conference for cybersecurity. We also organized an NYU Cognichip Hackathon, engaging students across 24 international teams in AI-assisted RTL design workflows. The GUIDE repository is open for contributions and available at: https://github.com/FCHXWH823/LLM4ChipDesign.

Problem

• GenAI
• Digital Design Education
• Courseware Repository
• RTL Design
• Hardware Security

Innovation

• GenAI
• RTL generation
• LLM-aided testbench
• hardware security
• open courseware