The 3rd International Workshop on Machine Learning for Software Hardware Co-Design (MLSH'23)

October 22nd, 2023
In conjunction with PACT'23 (Vienna, Austria)

Important Dates


As Machine Learning (ML) continues to permeate all areas of computing, software system designers and software stack developers are adopting ML solutions and designs to solve challenging problems presented in their areas; especially in areas like optimization and hardware design. ML is increasingly being used to solve a diverse set of problems such as the design of cost models, code optimization heuristics, efficient search space exploration, automatic optimization, and program synthesis. Designing accurate machine learning models, feature engineering, verification, and validation of obtained results and selecting and curating representative training data are all examples of challenging but important problems in this area that are actively being explored by a large community of researchers in industry and academia. This workshop provides a great venue for the international research community to share ideas and techniques to apply machine learning to system challenges with a focus on the software stack and hardware.


We will solicit papers on topics including, but not limited to, the following areas:

Submission Guidelines

We invite both full-length research papers and short research papers. The submitted paper should not exceed the page limit (8 pages for full-length and 4 pages for short papers) and should follow the IEEE conference proceedings templates. The page limit applies to all content NOT including references, and there is no page limit for references.

The submission will be reviewed by at least three program committee members and should not have been published in or under review for another venue. Accepted papers will be published in our online proceedings. Submit your paper using this link.


October 22nd at 1:30pm.

Time Presentation
1:30pm-1:35pm Opening Notes.
1:35pm-2:05pm Ondrej Sykora (Google), GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation.
2:05pm - 2:35PM Volker Seeker (Meta AI Research), Enabling Machine Learning Driven Heuristic Tuning in Compilers.
2:35pm - 3:05pm Afif Boudaoud, Smail Kourta, Massinissa Merouani and Riyadh Baghdadi (New York University Abu Dhabi), DeepOPT: Single-Shot Code Optimization Through Deep Learning.
3:05pm - 3:30pm Break.
3:30pm - 4:00pm Chris Cummins (Meta AI Research), Large Language Models for Compilers.
4:00pm - 4:30pm TBD.
4:30pm - 5:00pm TBD.
5:00pm - 5:05pm Closing notes.

Program Committee

Past Editions