International Workshop on Machine Learning for Software Hardware Co-Design (MLSH'20)
October 2nd, 2020
Virtual - In conjunction with PACT20
Paper submission: Extended - September 11th (AOE), 2020
- Paper notification: September 21st, 2020
- Camera-ready: October 2nd, 2020
- Workshop: October 2nd, 2020
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:
- ML for the software stack
- Heuristics and cost model construction.
- Optimization space exploration.
- Automatic code optimization.
- Bug detection.
- Program synthesis.
- Program and code representation.
- Important training paradigms.
- ML for hardware
- ML models for optimal configuration for FPGA.
- Load balancing between CPU and accelerators (e.g. GPUs, TPUs, etc).
- ML models to improve computer architecture design.
- Analysis and techniques to define meaningful representation (features) for compilers and hardware.
- Training data
- Exploring the availability or generation of efficient training data for compilers and hardware.
- Utilizing graph-based data for machine learning.
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).
|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).
- Abhinav Vishnu (AMD)
- Charith Mendis (MIT)
- Hugh Leather (Facebook)
- Shuaiwen Leon Song (University of Sydney)
- Martin Kong (University of Oklahoma)
- Mike Lang (Los Alamos National Laboratory)
- Mary Hall (University of Utah)
- Nathan Hodas (Pacific Northwest National Laboratory)
- Sameer Abu Asal (Facebook)
- Sridutt Bhalachandra (Lawrence Berkeley National Laboratory)
- Yufei Ding (University of California, Santa Barbara)
- Zheng Wang (University of Leeds)
- Wei Wang (Intel)