Solving previously unsolvable combinatorial problems with AI

On-Demand Webinar

Solving previously unsolvable combinatorial problems with AI

How do you find the best solution when faced with many choices? Combinatorial optimization is a field of mathematics that seeks to find the most optimal solutions for complex problems involving multiple variables. There are numerous business verticals that can benefit from combinatorial optimization, whether transport, supply chain, or the mobile industry.

More recently, we’ve seen gains from AI for combinatorial optimization, leading to scalability of the method, as well as significant reductions in cost. This method replaces the manual tuning of traditional heuristic approaches with an AI agent that provides a fast metric estimation.

In this presentation you will find out:

  • Why AI is crucial in combinatorial optimization
  • How it can be applied to two use cases: improving chip design and hardware-specific compilers
  • The state-of-the-art results achieved by Qualcomm AI Research

Overview

September 17th: Day 1 – Keynote & Presentations 8:00am-4:00pm
Location: Qualcomm – N Auditorium 5775 Morehouse Drive, San Diego, CA

September 17th: 4:30pm-6:30pm: Networking Meetings & Reception
Location: La Jolla Marriott 4240 La Jolla Drive, San Diego, CA

September 18th: Day 2: 8:30am – 12:30pm Presentations & Meetings
Location: La Jolla Mariott 4240 La Jolla Drive, San Diego, CA

Highlights

Opening Keynote:
Qualcomm EVP, Engineering & CTO Jim Thompson

The Internet of Things – Challenges and Opportunities:
Qualcomm Technologies SVP & GM, IoT Jeff Lorbeck

Industry 4.0/5G and Smart Cities:
Qualcomm Technologies VP, Business Development Jeffery Torrance

Qualcomm Smart Cities Accelerator Program:
Qualcomm Technologies Sr. Director, Business Development, Head of Smart Cities Sanjeet Pandit
Industry Panel:
Smart Cities Deployments: Experiences and Learnings

Industry Panel:
5G/Industry 4.0/Auto: Future Smart Spaces

City Panel:
Use Cases

Speakers

Chris Lott
Chris Lott

Senior Director of Engineering, Qualcomm AI Research
Chris has a PhD in Electrical Engineering Systems from the University of Michigan, with research emphasis on stochastic control and optimization. His early career focus was GPS R&D at Trimble, including a collaborative development of the first commercial GPS car navigation systems. He joined Qualcomm in 2001 and has led teams in 3G and 4G wireless system design, particularly for MAC layer and optimal network resource allocation. Since 2013 he has helped lead Qualcomm’s efforts into ML and AI, with important impact on hardware and software developments, and application areas like speech and automotive. Currently he leads the ML Optimization team at Qualcomm AI Research, with a focus on ML for compilers, graph nets, and combinatorial optimization.

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