Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Schedule: Hardware sessions

Developments in computing hardware deserve credit for helping spark a renaissance in artificial intelligence technologies such as deep learning. Learn about the latest advances in both GPU and CPU architectures, as well as alternative approaches like end-point processing.

4:00pm4:40pm Wednesday, June 28, 2017
Implementing AI
Location: Sutton North Level: Beginner
Shaoshan Liu (PerceptIn)
Average rating: *****
(5.00, 1 rating)
It is imperative to make high-profile technologies like AI affordable in order for these technologies to proliferate and to benefit the general public. Shaoshan Liu discusses PerceptIn's road to affordable AI-capable products. Read more.
4:00pm4:40pm Wednesday, June 28, 2017
Implementing AI
Location: Gramercy East/West Level: Intermediate
Qirong Ho (Petuum, Inc.)
Average rating: **...
(2.00, 1 rating)
Petuum, Inc. builds software that lets enterprises develop AI solutions in multiple programming languages and deploy them at scale and with high performance to internal, private computing resources that include a heterogeneous mix of workstations, clusters, CPUs, and GPUs. Qirong Ho outlines the architectural design choices and technical foundation needed to achieve these targets. Read more.
11:55am12:35pm Thursday, June 29, 2017
Implementing AI
Location: Beekman Level: Beginner
Michael B. Henry (Mythic)
Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment. Read more.
1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Beekman Level: Beginner
Xiaofan Xu (Intel), Cormac Brick (Intel)
Data is the “oxygen” of the AI revolution, but access to data on a large scale remains a luxury of an elite group of tech companies, effectively creating a “data wall” blocking smaller companies. Cormac Brick and Xiaofan Xu explore the problem of the data wall and offer a solution: synthetic datasets. Read more.