As experimental methods continue to evolve, generating increasingly complex and high-resolution datasets, machine learning (ML) is becoming an essential tool across numerous scientific disciplines. This conference will explore emerging ML methods and their applications in scientific discovery, focusing on processing technologies and strategies to accelerate deep learning and inference.
Fee Waivers: Thanks to generous support from our sponsors, the NSF HDR Institute AI-Accelerated Algorithms for Data-Driven Discovery (A3D3) and Exploring Neural Network Processors for AI in Science and Engineering (Voyager), we are offering in-person registration fee waivers (subject to funding availability) for graduate students on a first come first serve basis. If funds are still available, we will also open up fee waivers to undergraduate students and early-career researchers (<7 years since PhD) with financial need. Please indicate in the registration form if you are seeking a registration fee waiver and do not pay the fee.
Important Deadlines

• Abstract Submission: June 1, 2026
• (Optional) Extended Abstract Submission: June 15, 2026
• Early Bird Registration: July 15, 2026
  
  Topics include, but are not limited to:
  - Machine Learning Algorithm Design & Optimization
  - Accelerated Inference & Real-Time Processing
  - Scientific Applications of Fast ML
  - Scalable & Distributed ML Systems
  - Advanced Hardware & Computing Architectures 

We welcome abstracts for:

• Presentations and/or Posters
• • New for 2026: Submitters will have the option to submit a 4-page extended abstract (paper) to OpenReview (details to follow)
• Tutorials
• Topical (birds-of-a-feather) sessions

More information and registration details will follow. We look forward to welcoming you to San Diego this September!