Understanding the Challenges of Algorithm and Hardware Co-design for Deep Neural Networks

The co-design of algorithm and hardware has become an increasingly important approach for addressing the computational complexity of Deep Neural Networks (DNNs). There are several open problems and challenges in the co-design process and application; for instance, what metrics should be used to drive the algorithm design, how to automate the process in a simple way, how to extend these approaches to tasks beyond image classification, and how to design flexible hardware to support these different approaches. In this talk, we highlight recent and ongoing work that aim to address these challenges, namely energy-aware pruning and NetAdapt that automatically incorporate direct metrics such as latency and energy into the training and design of the DNN; FastDepth that extends the co-design approaches to a depth estimation task; and a flexible hardware accelerator called Eyeriss v2 that is computationally efficient across a wide range of diverse DNNs. 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 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 TI, 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 Engineering Emmy 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 2019 Edgerton Faculty Award, the 2018 Facebook Faculty Award, the 2018 & 2017 Qualcomm Faculty Award, the 2018 & 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 Outstanding 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/