Energy-Efficient Deep Learning: Challenges and Opportunities Slides

Energy-Efficient Deep Learning: Challenges and Opportunities Slides
Posted: 24 Oct 2018
Authors:
Vivienne Sze
Slides: 90

ABSTRACT
This talk will describe methods to enable energy-efficient processing for deep learning, specifically convolutional neural networks (CNN), which is the cornerstone of many deep-learning algorithms. Deep learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable and wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics and drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). We will give a short overview of the key concepts in CNNs, discuss its challenges particularly in the embedded space, and highlight various opportunities that can help to address these challenges at various levels of design ranging from architecture, implementation-friendly algorithms, and advanced technologies (including memories and sensors).

BIOGRAPHY OF SPEAKER
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 portable multimedia applications, including computer vision, deep learning, autonomous navigation, and video process/coding. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at Texas Instruments, 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 is a co-editor of the book entitled High Efficiency Video Coding (HEVC): Algorithms and Architectures (Springer, 2014). Prof. 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 received the Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT. She is a recipient of the 2017 Qualcomm Faculty Award, the 2016 Google Faculty Research Award, the 2016 AFOSR Young Investigator Research Program (YIP) 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 IEEE Micro Top Picks Award and the 2008 A-SSCC Outstanding Design Award.

For more information about research in the Energy-Efficient Multimedia Systems Group at MIT, visit http://www.rle.mit.edu/eems/.

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