ML for Computer Architecture and Systems

Call for Papers

Machine Learning for Computer Architecture and Systems is an interdisciplinary workshop that brings together researchers in computer architecture and systems and machine learning. This workshop is meant to serve as a platform to promote discussions between researchers in the workshop’s target areas. To achieve this, the format of the workshop will consist of a combination of keynote speakers, short talks,

followed by a panel discussion. Subject areas of the workshop included (but not limited to):

  • Learned models for computer architecture and systems optimization

  • Machine learning techniques for compiler and code optimization

  • Distributed systems for machine learning workloads

  • Machine learning for hardware/software co-design

  • Automated machine learning in EDA tools

  • Architecture and accelerator design for machine learning workloads

  • Evaluation of machine learning systems and architectures

  • Machine learning techniques for system and code performance estimation and optimization

Submission Instructions

  • We welcome submissions of up to 4 pages (not including references). This is not a strict limit, but authors are encouraged to adhere to it if possible.

  • All submissions must be in PDF format and should follow the ISCA'22 Latex Template.

  • Please follow the guidelines provided at ISCA 2022 Paper Submission Guidelines.

  • Starting this year, we will use the OpenReview system for the first time to enable an engaging and transparent review process inclusive of the whole community. While the review process is not public, we make the accepted papers and their reviews public after the notification deadline.

  • Please submit your paper at OpenReview.

  • Reviewing will be double blind: please do not include any author names on any submitted documents except in the space provided on the submission form.

Organizing Committee

  • Chris Cummins (Facebook AI Research)

  • Milad Hashemi (Google Research)

  • Akanksha Jain (Google)

  • Mangpo Phothilimthana (Google Research)

  • Paul Whatmough (ARM Research)

  • Neeraja J. Yadwadkar (University of Texas at Austin)

  • Amir Yazdanbakhsh (Google Research)

Contact us at