ML for Computer Architecture and Systems
Call for Papers
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
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.
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.
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 email@example.com