Call for Papers

Overview

Recent years have seen a surge of machine learning contributions across the chip design stack, tackling one of the most complex and consequential challenges in computer science today. Despite this momentum, the machine learning and electronic design automation (EDA) communities remain largely disconnected.

This workshop brings both communities together at a pivotal moment, when semiconductor design sits at the center of global economic and strategic priorities, hardware designer shortages are acute, and AI-driven design tools are approaching production readiness.

We invite submissions describing novel research, applications, systems, datasets, and position papers at the intersection of machine learning and chip/electronic design automation.

ImportantImportant Dates
Event Date
Paper & Poster Submission August 30, 2026 (AoE)
Notification September 29, 2026 (AoE)
Camera-ready Deadline October 9, 2026 (AoE)
Workshop December 12th, 2026, Paris

Topics of Interest

Topics include, but are not limited to:

  • Machine learning for physical design: placement, routing, and floorplanning
  • ML for RTL, logic synthesis, and technology mapping
  • Timing, power, and area prediction and optimization
  • Graph neural networks for circuits and netlists
  • Generative models (e.g., diffusion, flow matching) for layout and design
  • Reinforcement learning for EDA tasks, including script generation, testbench production, and module completion
  • Large language models and foundation models for hardware description languages (Verilog, VHDL, and SystemVerilog)
  • Agentic systems for chip design workflows with tool use (linters, synthesizers, simulators, timing analyzers, etc.)
  • Reproducible benchmarks, datasets, and open-source infrastructure
  • Societal, educational, and workforce aspects of AI-driven chip design

We particularly encourage submissions that promote reproducibility through open-source code, datasets, model weights, evaluation frameworks, or other openly available research artifacts.

Submission Instructions

Submissions will be managed through OpenReview.

Submission portal: OpenReview

Please use the official NeurIPS 2026 style files and formatting guidelines.

Review Process

Submissions will be evaluated based on:

  • Relevance to the workshop
  • Technical quality
  • Originality
  • Clarity
  • Potential to stimulate discussion and future research

Workshop Format

The workshop will be held as a full-day, 9-hour in-person event at NeurIPS 2026 and will feature:

  • Invited keynote talks
  • Contributed paper presentations
  • Poster sessions
  • Best Paper and Best Poster awards
  • A moderated panel discussion

Invited Speakers

Confirmed speakers include:

  • Xing Hu — Institute of Computing Technology, Chinese Academy of Sciences
  • Subhasish Mitra — Stanford University

Additional speakers will be announced soon.

Submission Details

Tracks

Track Length Format
Research Papers 7–9 pages NeurIPS format
Poster Papers 3–4 pages NeurIPS format

Submission Notes:

  • Page limits exclude references.
  • All submissions will receive at least three double-blind reviews.
  • This workshop is non-archival as per NeurIPS definition (papers won’t be available in NeurIPS proceedings). Accepted papers will be publicly posted with PDFs on OpenReview, which may be indexed by search engines and bibliographic databases.
  • Poster submissions may include position papers, exploratory work, highly experimental research, or applied research contributions.

AI Policies

This workshop follows the NeurIPS 2026 policies regarding the use of Large Language Models (LLMs).

For Authors: Authors may use LLMs in accordance with the NeurIPS 2026 policy. Authors remain fully responsible for the correctness, originality, and integrity of their submissions. Papers containing fabricated, hallucinated, or misleading content may be rejected.

For Reviewers: Reviewers must not use LLMs or other generative AI systems to review submissions or upload confidential manuscripts to external AI services. This workshop does not adopt the optional AI-assisted reviewing experiment described for parts of the NeurIPS 2026 main conference.