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Energy-efficient AI | Vivienne Sze | TEDxMIT
Jul 16, 2019
Quote:
Today, most of the processing for Artificial Intelligence (AI) happens in the cloud (i.e., data centers); however, there are many compelling reasons to perform the processing locally on the device (e.g., smartphones or robots) including reducing the dependence on communication infrastructure, preserving data privacy, and reducing reaction time.
One of the key limitations of local processing is energy consumption. Researchers are working on various techniques to enable energy-efficient AI, and how energy-efficient AI extends the reach of AI beyond the cloud to enable a wide range of applications from robotics to health care.
Vivienne Sze received the B.A.Sc. (Hons) degree in electrical engineering from the University of Toronto, Toronto, ON, Canada, in 2004, and the S.M. and Ph.D. degree in electrical engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, in 2006 and 2010 respectively. She received the Jin-Au Kong Outstanding Doctoral Thesis Prize for her Ph.D. thesis in electrical engineering at MIT in 2011.
She is an Associate Professor in the Electrical Engineering and Computer Science Department at MIT. Her research interests include energy efficient algorithms and architectures for portable multimedia applications. From September 2010 to July 2013, she was a Member of Technical Staff in the Systems and Applications R&D Center at Texas Instruments (TI), Dallas, TX, where she designed low-power algorithms and architectures for video coding. She also represented TI in the JCT-VC committee of ITU-T and ISO/IEC standards body during the development of High Efficiency Video Coding (HEVC), which received a Primetime Emmy Engineering Award. She co-edited a book entitled High Efficiency Video Coding (HEVC) - Algorithms and Architecture (Springer, 2014).
She was a recipient of the 2017 Qualcomm Faculty Award, 2016 Google Faculty Research Award, the 2016 AFOSR Young Investigator Research Program Award, the 2016 3M Non-Tenured Faculty Award, the 2014 DARPA Young Faculty Award, the 2007 DAC/ISSCC Student Design Contest Award and a co-recipient of the 2017 CICC Best Invited Paper Award, the 2016 Micro Top Picks Award and the 2008 A-SSCC Outstanding Design Award.
https://youtu.be/WbLQqPw_n88
Efficient Computing for Deep Learning, Robotics, and AI (Vivienne Sze) | MIT Deep Learning Series
Jan 23, 2020
Quote:
Lecture by Vivienne Sze in January 2020, part of the MIT Deep Learning Lecture Series.
Outline:
0:00 - Introduction
0:43 - Talk overview
1:18 - Compute for deep learning
5:48 - Power consumption for deep learning, robotics, and AI
9:23 - Deep learning in the context of resource use
12:29 - Deep learning basics
20:28 - Hardware acceleration for deep learning
57:54 - Looking beyond the DNN accelerator for acceleration
1:03:45 - Beyond deep neural networks