Add DATE 2025 conference paper on GUIDE educational framework

[Copilot]

Creates a 6-page IEEE-format paper for DATE 2025 special session on LLMs in teaching, documenting the GUIDE (GenAI-based University Instructional collateral for Design and EDA) framework and its impact on student competencies.

Paper Content

Papers/DATE2025/main.tex - Complete LaTeX paper covering:

• Framework architecture: customizable syllabus, 18 Google Colab modules, benchmark datasets, assessment materials
• Student competencies developed: prompt engineering, critical evaluation, verification rigor, security awareness
• Deployment data: 40% faster design productivity, 35% time spent on verification (vs 20% historically), 85% engagement
• Short/long-term implications: curriculum adaptation, competency evolution toward verification and architecture

Papers/DATE2025/references.bib - 35+ citations spanning LLM hardware design research, educational frameworks, and domain fundamentals

Supporting Materials

• README.md - Compilation instructions and paper overview
• Makefile - Build automation (make compiles paper)
• SUBMISSION_GUIDE.md - Submission checklist mapping paper sections to DATE requirements
• .gitignore - Excludes LaTeX auxiliary files (*.aux, *.log, etc.)

Modules Documented

18 instructional modules across digital/analog design, verification, and security:

• RTL generation: AutoChip, ChipChat, ROME (hierarchical), VeriThoughts (reasoning), Veritas (CNF-based)
• Verification: Testbench generation, Hybrid-NL2SVA, security assertions
• Specialized: PrefixLLM (optimization), C2HLSC (HLS), Masala-CHAI (analog), LLMPirate (IP security)
• Research: VeriContaminated (contamination), SALAD (unlearning)

Paper compiles successfully to 6 pages with all references resolved.

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Original prompt

Now I was invited to submit a paper to conference DATE (Design, Automation & Test in Europe Conference) special session about bringing your insights and comments on the use of LLMs in teaching. The conference is particularly interested in the implications on the competences students develop and possible impacts not only in the short but also long-term. Now given a rough abstract:
GUIDE: GenAI-based University Instructional collateral for Design and EDA

GUIDE is a classroom-ready courseware for teaching GenAI in EDA. It packages customizable syllabus, slide decks, stackable Colab modules, and benchmark datasets spanning digital and analog flows: NL→RTL synthesis, assertion/testbench generation, hierarchical prompting, formal and property checks, and security (e.g., hardware Trojans, IP risks). Students run prompts, simulation, and verification in the browser against curated benchmarks; instructors get rubrics, project prompts, and assessment aids. GUIDE drops cleanly into VLSI/EDA courses, bootcamps, or upskilling modules, scaffolding skills from prompt engineering to verification and security analysis using open-sourced resources.
Author list: Weihua Xiao (NYU), Jason Blocklove (NYU), Johann Knechtel (NYU-AD), Ozgur Sinanoglu (NYU-AD), Kanad Basu (RPI), J. Rajendran (TAMU), Siddharth Garg (NYU), and Ramesh Karri (NYU).

Please write a 6-page paper (latext version) for me about this github repo: generative AI for chip design.

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