ML for Computer Architecture and Systems
(Co-Located with ASSYST)
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 (AutoML for Hardware)
Automated machine learning in EDA tools
Architecture and accelerator design for machine learning workloads
Evaluation of deployed machine learning systems and architectures
Benchmarking and comparison between machine learning algorithms and heuristics
Learned models for improving environmental sustainability
Acceleration of graph learning applications and biomedical applications
Representation learning of computer system and chip
Intractability and causality in learned models for computer architecture and systems
Composable systems and disaggregation of resources for machine learning applications
Computational storage in machine learning environment
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'23 Latex Template.
Please follow the guidelines provided at ISCA 2023 Paper Submission Guidelines.
Please submit your paper at OpenReview. While the review process is not public, we make the accepted papers and their reviews public after the notification deadline.
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.
Shoaib Akram (Australian National University)
Ismail Akturk (Ozyegin University)
Murali Emani (Argonne National Laboratory)
Qijing Huang (NVIDIA)
Pooyan Jamshidi (University of South Carolina)
Pratik Mishra (AMD Research)
Mangpo Phothilimthana (Google Research, Brain Team)
Ananda Samajdar (IBM Research)
Nishit Shah (Microsoft)
Tianqi Tang (Meta)
Neeraja J. Yadwadkar (University of Texas, Austin)
Amir Yazdanbakhsh (Google Research, Brain Team)
Contact us at firstname.lastname@example.org