"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?