"Architecture 2.0 is a community-driven ecosystem that employs machine learning to minimize human intervention and build more complex, efficient computer systems in a shorter timeframe."
Event Overview
ML-driven architecture research holds great promise. But it also poses several challenges that we must understand and tackle collectively. The figure below illustrates some of the major challenges:
The goal of this virtual event is to identify the next generation of machine learning algorithms, datasets, tools, infrastructure, best practices, education and workforce training required to drive progress in our community.
Date and Time
When: September 15 from 9am PT to 1pm PT
Where: Virtual event (will be recorded), Link for Zoom Webinar Registration
Who: Open to the community
Registration
Please fill out this form for registration to the workshop.
Agenda
The schedule for the event, all times in PT:
09:00 AM - 09:15 AM Welcome & Overview of Architecture 2.0
09:15 AM - 09:30 AM Lightning Talks: Breakout Themes
09:30 AM - 10:00 AM Keynote: Partha Ranganathan (Google)
10:00 AM - 10:50 AM Breakout 1 (see details below)
10:50 AM - 11:00 AM Break
11:00 AM - 11:50 AM Breakout 2 (see details below)
12:00 PM - 12:50 PM Breakout Report
12:50 PM - 1:00 PM Next Steps/White paper discussion/Conclusion
Topics of Discussion
The workshop aims to foster lively discussions on Architecture 2.0 to identify key workstreams necessary for advancing the field. To achieve this goal, we have organized breakout groups covering various relevant topics, encouraging active participation and engagement among participants.
Technical Considerations
Datasets
What datasets do we need?
How should we collect these datasets for architecture research?
What metadata should the datasets contain to enable broad usage?
How do we create standard data formats from any ML algorithm?
ML Algorithms
How can we learn and apply new ML algorithms to effectively design high-performance/efficient systems?
How do we make our community more accessible to ML researchers?
How do we embrace ML algorithm design as part of architecture research?
Tools & Infra
How do we reduce the sim2real gap?
What instrumentation mechanisms do we need for creating the datasets?
What gym environments do we need to enable data-centric AI?
How do we define standard data formats for interoperability?
Best Practices
Can we create a systematic playbook for best-known methods?
How do we ensure strong baselines and reproducibility?
Collaboration and Outreach
Industry Relations
How do we share traces/infrastructure without IP leakage?
What resources can we get from the industry?
How can industry contribute to academia?
How can academia tech transfer to the industry?
Workforce Training
What do future architects need to know in addition to conventional architecture background?
How should we teach ML to architecture students so that they have the necessary foundations?
Related Links
Committee
Organizers & Workstream Leads
Siddharth
Garg
NYU
Brian
Hirano
Micron
Jenny
Huang
NVIDIA
Tushar
Krishna
GATech
Srivatsan
Krishnan
Harvard
Benjamin
Lee
UPenn
Yingyan (Celine)
Lin
GATech
Jason
Lowe-Power
UC Davis
Martin
Maas
Google Deepmind
Shvetank
Prakash
Harvard
Vijay
Janapa Reddi
Harvard
Matt
Sinclair
U. Wisconsin-Madison
Srinivas
Sridharan
NVIDIA
Amir
Yazdanbakhsh
Google Deepmind
Jason
Yik
Harvard
Cliff
Young
Google Deepmind
workshop Recordings
Welcome & Introduction
![](https://www.google.com/images/icons/product/drive-32.png)
Partha Ranganathan's Keynote
![](https://www.google.com/images/icons/product/drive-32.png)
Breakout Reports
![](https://www.google.com/images/icons/product/drive-32.png)
Wrap-Up & Next Steps
![](https://www.google.com/images/icons/product/drive-32.png)
Contact Us
Please email contact.architecture2.0@gmail.com if you have any questions or concerns.