Workshop on Systems for Next-Gen AI Paradigms

Jason Yik · Brian Anderson · Charlotte Frenkel · Vijay Janapa Reddi · Zergham Ahmed

Room 240
[ Abstract ] Workshop Website
Thu 8 Jun, 6:50 a.m. PDT

The workshop begins at 9:50am in Room 240. Please see our schedule on our website.

Deep learning methods have made great strides in machine intelligence over the past few
years, but they are now having trouble keeping up with the growing amount of data and
resources. As traditional system architectures get closer to their physical limits, the problem of
compute scalability is getting worse, which makes it hard to predict how far AI methods and
systems can go in the future. These issues beg the question: What are alternative directions for
the next-generation of AI methods and systems that will run them?

Processing domains like analog, asynchronous, event-based, probabilistic, neuromorphic,
photonic, and quantum computing have all shown promise for faster, more efficient AI with new
capabilities through a complete shift in the way AI systems work.

The goal of this workshop is to kick off discussions about next-generation systems and methods that will help AI move forward, specifically through a realistic assessment of how these exotic emerging approaches for next-generation AI are making progress toward practical relevance and in what timeframes.

We want to help both experts and non-experts, believers and doubters, by achieving the
following goals:
(1) Educate about new processing technology and AI methods on the horizon.
(2) Evaluate the strengths and paths to practical viability of different approaches.
(3) Discuss methods to compare next-generation systems against traditional systems and
against each other.
(4) Inspire the integration of new technologies toward future AI methods and systems.

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Timezone: America/Los_Angeles »