AI model efficiency is crucial for making AI ubiquitous, leading to smarter devices and enhanced lives. Besides the performance benefit, quantized neural networks also increase power efficiency for two reasons: reduced memory access costs and increased compute efficiency.
The quantization work done by the Qualcomm AI Research team is crucial in implementing machine learning algorithms on low-power edge devices. In network quantization, we focus on both pushing the state-of-the-art (SOTA) in compression and making quantized inference as easy to access as possible. For example, our SOTA work on oscillations in quantization-aware training that push the boundaries of what is possible with INT4 quantization. Furthermore, for ease of deployment, the integer formats such as INT16 and INT8 give comparable performance to floating point, i.e., FP16 and FP8, but have significantly better performance-per-watt performance. Researchers and developers can make use of this quantization research to successfully optimize and deploy their models across devices with open-sourced tools like AI Model Efficiency Toolkit (AIMET).
In this webinar you will learn about:
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
Principal Engr./Mgr. in the Corp. R&D AI Research team
Chirag Patel is a Principal Engr./Mgr. in the Corp. R&D AI Research team at Qualcomm Technologies, Inc. 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 a combination of machine learning and low power, always-on sensors, and forward-looking technologies. 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 a Ph.D. in Electrical Engineering from Georgia Institute of Technology, Atlanta.
Director of Engineering at Qualcomm Technologies Netherlands BV
Tijmen Blankevoort is a Director of Engineering at Qualcomm Technologies Netherlands BV. 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.
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Email: [email protected]