Vivienne Sze (MIT)
Biography: Vivienne Sze is an associate professor of electrical engineering and computer science at MIT. She is also the director of the Energy-Efficient Multimedia Systems research group at the Research Lab of Electronics (RLE). Sze works on computing systems that enable energy-efficient machine learning, computer vision, and video compression/processing for a wide range of applications, including autonomous navigation, digital health, and the internet of things. She is widely recognized for her leading work in these areas and has received many awards, including the AFOSR and DARPA Young Faculty Award, the Edgerton Faculty Award, several faculty awards from Google, Facebook, and Qualcomm, the 2018 Symposium on VLSI Circuits Best Student Paper Award, the 2017 CICC Outstanding Invited Paper Award, and the 2016 IEEE Micro Top Picks Award. As a member of the JCT-VC team, she received the Primetime Engineering Emmy Award for the development of the HEVC video compression standard. She co-author of the recent book entitled “Efficient Processing of Deep Neural Networks” (Morgan & Claypool, 2020). For more information about research in the Energy-Efficient Multimedia Systems Group at MIT visit: http://www.rle.mit.edu/eems/
How to Evaluate Efficient Deep Neural Network Approaches
[Aug 27 2020 9.30-10.30. Prerecorded talk and live Q&A (10:10-10:30); Slides . Video coming soon]
Abstract: Enabling the efficient processing of deep neural networks (DNNs) has become increasingly important to enable the deployment of DNNs on a wide range of platforms, for a wide range of applications. To address this need, there has been a significant amount of work in recent years on designing DNN accelerators and developing approaches for efficient DNN processing that spans the computer vision, machine learning, and hardware/systems architecture communities. This talk will focus on *how* to evaluate these different approaches, which include the design of DNN accelerators and DNN models. It will also highlight the key metrics that should be measured and compared and present tools that can assist in the evaluation.