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01 Mar 2021
Abstract - Visual object detection and recognition are needed for a wide range of applications including robotics/drones, self-driving cars, smart Internet of Things, and
portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy or latency concerns. This talk will describe
methods to enable energy-efficient processing of deep convolutional neural networks (CNN), as such networks form the cornerstone of many deep-learning
algorithms. While CNNs deliver record-breaking accuracy for many computer vision tasks, they require significant compute resources due to the size of the networks
(e.g., hundreds of megabytes for filter weights storage and 30k-600k operations per input pixel). We will give a short overview of the key concepts in CNNs, discuss
the computational challenges CNNs present, particularly in the embedded space, and highlight various opportunities where hardware designers can help to address
these challenges.
Bio - Vivienne Sze is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department. Her research interests include energy-aware signal
processing algorithms, and low-power circuit and system design for multimedia applications such as computer vision, autonomous navigation, machine learning
and video compression. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at TI, where she developed algorithms and hardware for the
latest video coding standard H.265/HEVC. She is a co-editor of the book entitled, “High Efficiency Video Coding (HEVC): Algorithms and Architectures” (Springer,
2014).
Dr. Sze received the B.A.Sc. degree from the University of Toronto in 2004, and the S.M. and Ph.D. degree from MIT in 2006 and 2010, respectively. In 2011, she
was awarded the Jin-Au Kong Outstanding Doctoral Thesis Prize in electrical engineering at MIT for her thesis on “Parallel Algorithms and Architectures for Low
Power Video Decoding”. She is a recipient of the 2017 Qualcomm Faculty Award, 2016 Google Faculty Research Award, 2016 AFOSR Young Investigator Award,
2016 3M Non-tenured Faculty Award, 2014 DARPA Young Faculty Award, 2007 DAC/ISSCC Student Design Contest Award and a co-recipient of the 2016 MICRO
Top Picks Award and 2008 A-SSCC Outstanding Design Award.
portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy or latency concerns. This talk will describe
methods to enable energy-efficient processing of deep convolutional neural networks (CNN), as such networks form the cornerstone of many deep-learning
algorithms. While CNNs deliver record-breaking accuracy for many computer vision tasks, they require significant compute resources due to the size of the networks
(e.g., hundreds of megabytes for filter weights storage and 30k-600k operations per input pixel). We will give a short overview of the key concepts in CNNs, discuss
the computational challenges CNNs present, particularly in the embedded space, and highlight various opportunities where hardware designers can help to address
these challenges.
Bio - Vivienne Sze is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department. Her research interests include energy-aware signal
processing algorithms, and low-power circuit and system design for multimedia applications such as computer vision, autonomous navigation, machine learning
and video compression. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at TI, where she developed algorithms and hardware for the
latest video coding standard H.265/HEVC. She is a co-editor of the book entitled, “High Efficiency Video Coding (HEVC): Algorithms and Architectures” (Springer,
2014).
Dr. Sze received the B.A.Sc. degree from the University of Toronto in 2004, and the S.M. and Ph.D. degree from MIT in 2006 and 2010, respectively. In 2011, she
was awarded the Jin-Au Kong Outstanding Doctoral Thesis Prize in electrical engineering at MIT for her thesis on “Parallel Algorithms and Architectures for Low
Power Video Decoding”. She is a recipient of the 2017 Qualcomm Faculty Award, 2016 Google Faculty Research Award, 2016 AFOSR Young Investigator Award,
2016 3M Non-tenured Faculty Award, 2014 DARPA Young Faculty Award, 2007 DAC/ISSCC Student Design Contest Award and a co-recipient of the 2016 MICRO
Top Picks Award and 2008 A-SSCC Outstanding Design Award.
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