Energy-Efficient Machine Learning and Applications
Qualcomm AI Research, Qualcomm Technologies, Inc.
Chairman, Board of Directors
Recent progress in computing hardware, machine learning algorithms, and networks, as well as the availability of large datasets for model training, have created a strong momentum in development and deployment of game-changing AI applications. Intelligent devices with human-like senses have enabled a variety of new use cases and applications, transforming the way we interact with each other and our surroundings. Dedicated hardware becomes tiny and very energy efficient (with mW or less power consumption), algorithms and models smaller (down to 10s of kB of memory requirements), and software lighter. This enormous technology wave and fast-growing ecosystem create a strong momentum toward new applications and business opportunities. On the other side of the physical world spectrum, sensors are becoming more sophisticated, able to sense a variety of modalities (vision, sound, environmental, motion/vibrations, etc.) and are being deployed in billions. This presentation will review the state-of-the-art in energy-efficient machine learning (including hardware, algorithmic, and software framework aspects), describe some examples of technologies and products, and illustrate use cases (including some display power-saving use cases). We will highlight Always-on Computer Vision, technology and product pioneered by Qualcomm, which combines innovations in the system architecture, ultra-low power designs, and dedicated hardware for CV algorithms running at the “edge.” With low end-to-end power consumption (less than 1 mW), tiny form factor, and low cost, always-on computer vision modules can be integrated into a wide range of battery- and line-powered devices (IoT, mobile/laptop, VR/AR, automotive, etc.), performing object detection, feature recognition, change/motion detection, and other applications.
Evgeni Gousev is a senior director of Qualcomm AI Research. He leads Qualcomm’s R&D organization in the Bay area and is also responsible for developing ultra-low-power embedded computing platforms, including always-on machine vision. He serves as the chairman of the voard of directors of tinyML Foundation (www.tinyML.org), a non-profit organization of more than 14k professionals in 37 countries worldwide. The foundation is focused on supporting and nurturing the fast-growing branch of ultra-low-power machine learning technologies and approaches dealing with machine intelligence at the very edge. Evgeni joined Qualcomm in 2005 and led technology R&D in the MEMS Research and Innovation Center, commercializing mirasol display technology. He earned a PhD in solid-state physics and an MS in applied physics. After graduation, Gousev joined Rutgers University, first as a postdoctoral fellow and then as a research assistant professor. While at Rutgers, he performed fundamental research in the area of advanced gate dielectric for CMOS devices, which, a decade later, became industry-wide standards. In 1997, he was a visiting professor with the Center for Nanodevices and Systems, Hiroshima University, Japan. Shortly after, he joined IBM, where he led projects in the field of advanced silicon technologies at the Semiconductor Research and Development Center in East Fishkill and T.J. Watson Research Center in Yorktown Heights, NY. He has co-edited 26 books and published more than 166 papers (with over 11k citations and h-index of 49: Google Scholar). He is a holder of more than 100 issued and filed patents. Gousev is a member of several professional boards, committees, panels, and societies. In 2020, he was inducted into the “Hall of Fame” of SEMI MEMS and Sensors Industry Group.