The 2nd International Workshop on Machine Learning for Software Hardware Co-Design (MLSH'21)

September 26'th, 2021
Virtual - In conjunction with PACT21

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 published in or under review for another venue. Accepted papers will be published in our online proceedings. Submit your paper using this link.


September 26th from 11am ET to 1:50pm ET (Eastern Time).

Time (ET) Presentation
11:00-11:05AM Opening Notes.
11:05-11:35AM Marco Minutoli (PNNL)
SODA: Agile Hardware Design for Specialized Systems.
11:35 - 12:05PM Anup Das (Drexel University)
Intelligent Software for Intelligent Machines.
12:05 - 12:15PM Break.
12:15 - 12:45PM Massinissa Merouani (New York University Abu Dhabi)
A Deep Learning Based Cost Model for Automatic Code Optimization.
12:45-1:15PM Jordi Armengol-Estape (University of Edinburgh)
Learning C To X86 Translation: an Experiment in Neural Compilation.
1:15 - 1:45PM Yundi Qian, Mircea Trofin (Google)
MLGO: Machine Learning Guided (Compiler) Optimization.
1:45 - 1:50PM Closing notes.

How to Attend?

All the presentations will be virtual. To attend, please join the following Zoom Link (Meeting ID: 977 4123 9295, Passcode: 624270).

Past Editions