Research Working Group
- Overview
- Training Working Group
- Inference Working Group
- Datasets Working Group
- Best Practices Working Group
- Research Working Group
Mission
Our mission is to build a collaborative academic-industry community with shared research infrastructure and sponsored support for accelerating machine learning innovation for everyone.
Purpose
Over the past year, the MLCommons® Research working group has made significant progress towards its original mission with several papers, tutorials, and new initiatives. Building on this success and the launch of MLCommons, we will keep accelerating the progress towards our mission and hope to positively impact the field of ML.
Deliverables
- Datasets
- HPC Benchmark
- Best Practices
- New Metrics
- Methodology
- New Workloads
- Workshops/Tutorials
Meeting Schedule
New schedule TBD.
How to Join
Use this link to request to join the group/mailing list, and receive the meeting invite:
Research Google Group.
Requests are manually reviewed, so please be patient.
Working Group Resources
- Shared documents and meeting minutes:
- Associate a Google account with your e-mail address.
- Ask to join our Public Google Group.
- Once approved, go to the Research folder in our Public Google Drive.
Working Group Chairs
Gennady Pekhimenko (gennady@mlcommons.org) - CV
Gennady Pekhimenko is an Assistant Professor at the University of Toronto, CS department and (by courtesy) ECE department, where he is leading the EcoSystem (Efficient Computing Systems) group. Gennady is also a Faculty Member at Vector Institute and a CIFAR AI chair. Before joining Univ. of Toronto, he spent a year in 2017 at Microsoft Research in Redmond in Systems Research group. He got his PhD from the Computer Science Department at Carnegie Mellon University in 2016. Gennady is a recipient of Amazon Machine Learning Research Award, Facebook Faculty Research Award, Connaught New Researcher Award, NVIDIA Graduate, Microsoft Research, Qualcomm Innovation, and NSERC CGS-D Fellowships. His research interests are in the areas of computer architecture, hardware acceleration, systems for machine learning, and compilers.
Vijay Janapa Reddi (vj@mlcommons.org) - CV
Vijay Janapa Reddi is an Associate Professor at Harvard University. Before joining Harvard, he was an Associate Professor at The University of Texas at Austin in the Department of Electrical and Computer Engineering. His research interests include computer architecture and runtime systems, specifically in the context of autonomous machines and mobile and edge computing systems. Dr. Janapa Reddi has received multiple honors and awards, including the National Academy of Engineering (NAE) Gilbreth Lecturer Honor and has been inducted into the MICRO and HPCA Halls of Fame. He received a Ph.D. in computer science from Harvard University, M.S. from the University of Colorado at Boulder and B.S from Santa Clara University.