LAD Fellowship

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LAD Fellowship – Sponsored by the IEEE International Conference on LLM-Aided Design (ICLAD)

The IEEE International Conference on LLM-Aided Design (ICLAD) is pleased to announce the establishment of the LAD Fellowship, a prestigious award designed to support outstanding graduate students pursuing research in design automation leveraging large language models (LLMs). With the rapid emergence of LLMs and their transformative applications in hardware and software design, the LAD Fellowship aims to encourage bold, innovative research at the intersection of AI, automation, and systems.

Each fellowship carries a cash award of $10,000, provided directly to the selected student. Fellows are expected to participate in the following year’s LAD Conference, where they will present their research progress through a poster session. A portion of the fellowship award should be used to support travel and participation in the conference.

Application Materials

Student applicants must submit the following:

1. A 3-page research proposal outlining their project. (Guidelines below)
2. The applicant’s curriculum vitae (CV) highlighting academic achievements, publications, and relevant experience.
3. Two letters of recommendation, including one from the applicant’s primary research advisor.

Guidelines for the 3-Page Proposal

The research proposal should be no more than 3 single-spaced pages (excluding references). Suggested structure:

• Title and Abstract (½ page): A clear, concise description of the research problem and goals.
• Background and Motivation (½–1 page): Context within the design automation and LLM-Aided Design landscape; why this problem matters.
• Research Objectives and Methods (1–1.5 pages): Specific aims, methodology, and expected innovations. Include how LLMs or related techniques are being applied or extended.
• Expected Impact (½ page): Potential contributions to the LLM-Aided Design field and the broader design automation community.
• References (not included in page count): Citations to prior work as appropriate.

Clarity, originality, and feasibility will be emphasized. Proposals should be written for a technical but broad design automation audience. The duration of the proposed research is one year.

While the LAD Fellowship welcomes proposals across the full spectrum of design automation research, the following research direction is especially encouraged because of its timeliness and strategic importance.

AI Benchmarks for End-to-End Chip Design. While AI and LLMs are rapidly transforming electronic design automation, the field still lacks standardized, high-quality benchmarks for objectively evaluating and comparing AI-driven design methodologies. This research direction aims to develop a comprehensive suite of challenging benchmark designs spanning the entire chip design flow, including RTL generation, IP integration, functional verification, logic synthesis, physical design, timing and power closure, and design-for-test. Rather than focusing on isolated tasks, these benchmarks should capture realistic industry-level design complexity, cross-stage dependencies, and progressively increasing levels of difficulty. Accompanied by well-defined evaluation metrics, they would establish a rigorous AI Index for Chip Design, enabling objective assessment of design quality, correctness, productivity, scalability, and automation across diverse AI- and LLM-driven design tools. Such an open benchmark suite would not only accelerate innovation and fair comparison within the research community, but also serve as a common foundation for academia and industry to drive the next generation of intelligent EDA systems.

Applicants interested in this special research direction are encouraged to contribute to one or more components of this benchmark ecosystem. Example topics include developing challenging benchmark designs for specific stages of the chip design flow, creating realistic design scenarios, defining quantitative evaluation metrics, building automated benchmarking and scoring frameworks, and establishing standardized evaluation protocols for AI- and LLM-driven design tools. Preliminary results or proof-of-concept implementations that demonstrate the feasibility and potential impact of the proposed research will be viewed favorably. Collectively, these contributions will help establish a community-wide benchmark and evaluation standard that enables rigorous comparison, accelerates innovation, and drives the next generation of AI-assisted chip design.

Selection Criteria

Fellowships will be awarded on a merit basis, with emphasis on:

• Academic excellence and credentials of the student.
• Quality, originality, and relevance of the proposed research to LLM-Aided Design.
• Potential impact of the research on the broader design automation community.

Applicants should be full-time graduate students who still have at least one year remaining in their program before graduation.

Timeline

• Application Submission Opens: August 20, 2026
• Application Deadline: September 27, 2026, AoE
• Award Notification: November 10, 2026

Application submission portal are below.

About the LAD Fellowship

The LAD Fellowship underscores ICLAD’s commitment to supporting the next generation of research leaders in design automation leveraging the most advanced AI technologies. By investing in graduate students, this program seeks to accelerate advances in LLM-driven innovation, foster academic–industry collaboration, and strengthen the global community working at the intersection of AI, hardware, and software design.

link for students

link for recommendation letters submission