Intelligence at Scale through AI Model Efficiency

Tuesday, April 06, 2021 | 9:00 AM PDT

Intelligence at Scale through AI Model Efficiency

Artificial Intelligence (AI), specifically deep learning, is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, the deep neural networks of today use too much memory, compute, and energy. To make AI truly ubiquitous, it needs to run on the end device within tight power and thermal budgets. Advancements in multiple areas are necessary to improve AI model efficiency, including quantization, compression, compilation, and neural architecture search (NAS). In this webinar, we’ll discuss:

  • Qualcomm AI Research’s latest model efficiency research
  • Our new NAS research to optimize neural networks more easily for on-device efficiency
  • How the AI community can take advantage of this research though our open-source projects, such as the AI Model Efficiency Toolkit (AIMET) and AIMET Model Zoo

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

Tijmen Blankevoort
Tijmen Blankevoort

Engineer, Sr Staff/Manager,
Qualcomm Technologies Netherlands B.V.

Tijmen Blankevoort is the team lead for compression and quantization research at Qualcomm Technologies Netherlands B.V. With a background in mathematics and artificial intelligence, he started a deep learning start-up in 2013 with professor Max Welling, which was later acquired by Qualcomm Technologies in 2017. The compression and quantization research team focuses on making neural network models more efficient, ensuring that low-bit quantization can be achieved through an automatic process without sacrificing much accuracy. Tijmen and his team are conducting new research in this area, and simultaneously bridging the gap between research and practice. In his spare time, Tijmen loves to play Magic: The Gathering, and is a fervent molecular gastronomy cook.

Chirag Patel
Chirag Patel

Engineer, Principal/Manager,
Qualcomm Technologies, Inc.

Chirag Patel is currently a Principal Engr./Mgr. in Corp. R&D AI Research team at Qualcomm Technologies, Inc. (QTI). As AI Model Efficiency Toolkit (AIMET) project lead, he is responsible for bringing neural network model efficiency R&D to practice working with inter-disciplinary teams, feature roadmap planning, and customer engagements. He also leads projects for enriching smartphone UX experience using combination of machine learning and low power, always-on sensors and forward-looking technologies. Prior to this, he has 10+ years of experience in wireless communications as a research engineer, leading design & standardization of standalone LTE in unlicensed spectrum and 3G/4G small cell technologies. He holds Ph.D. in Electrical Engineering from Georgia Institute of Technology, Atlanta.


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