Energy-Efficient Hardware for Embedded Vision and Deep Learning
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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; thus energy-efficiency is an important requirement in addition to real-time and robust performance. However, compared to traditional image processing tasks such as image and video compression, object detection and recognition use significantly higher dimensional data, and require more computation, leading to significant energy costs.
In this talk, we will describe how joint algorithm and hardware design can be used to reduce the energy consumption of object detection and recognition while delivering real-time and robust performance. We will discuss several energy-efficient techniques that increase sparsity, reduce memory costs and data movement, and show how they can be applied to popular forms of object detection and recognition, including those that use handcrafted features and deep convolutional neural nets. We will present results from recently fabricated ASICs (e.g. our deep learning accelerator called "Eyeriss") that demonstrate these techniques in real-time computer vision systems.
Vivienne Sze is an Assistant 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. 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 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 2008 A-SSCC Outstanding Design Award.