International Workshop on Machine Learning for Software Hardware Co-Design (MLSH'20)

October 2nd, 2020
Virtual - In conjunction with PACT20

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.


October 2nd from 12pm ET to 5pm ET (Eastern Time).

Time (ET) Presentation
12:00-12:05PM Opening Notes.
12:05-12:50PM Antonino Tumeo (PNNL)
Intelligent Design Space Exploration for High-Level and System Synthesis - Abstract.
12:50 - 1:30PM Saman Amarasinghe (MIT)
Compiler 2.0: Using Machine Learning to Modernize Compiler Technology.
1:30 - 1:35PM Break.
1:35 - 2:20PM Murali Emani (ANL)
Towards Understanding the Complex Correlation between ML and Systems - Abstract.
2:20-3:05PM Chris Cummins (Facebook AI Research)
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis - Abstract.
3:05 - 3:15PM Break.
3:15 - 3:35PM Jiajia Li (PNNL)
Generic, Sparse Tensor Core for Neural Networks - Abstract.
3:35 - 4:20PM Yang You (National University of Singapore)
Fast and Accurate Deep Neural Network Training - Abstract.
4:20 - 5:00PM Discussion and closing.

How to Attend?

All the presentations will be virtual. To attend, please join the following Zoom Link (note that this link is different from the previous link).

Program Committee