This tutorial describes methods to enable efficient processing for deep neural networks (DNNs), which are used in many AI applications including computer vision, speech recognition, robotics, etc. While DNNs deliver best-in-class accuracy and quality of results, it comes at the cost of high computational complexity. Accordingly, designing efficient algorithms and hardware architectures for deep neural networks is an important step towards enabling the wide deployment of DNNs in AI systems (e.g., autonomous vehicles, drones, robots, smartphones, wearables, Internet of Things, etc.), which often have tight constraints in terms of speed, latency, power/energy consumption, and cost.
In this tutorial, we will provide a brief overview of DNNs, discuss the tradeoffs of the various hardware platforms that support DNNs including CPU, GPU, FPGA and ASICs, and highlight important benchmarking/comparison metrics and design considerations for evaluating the efficiency of DNNs. We will then describe recent techniques that reduce the computation cost of DNNs from both the hardware architecture and network algorithm perspective. Finally, we will also discuss how these techniques can be applied to a wide range of image processing and computer vision tasks.
Vivienne Sze (MIT)
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 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) and a co-author of the forthcoming book entitled "Efficient Processing of Deep Neural Networks" (Morgan & Claypool, 2020). She is a Program Co-chair of the [2020 Conference on Machine Learning and Systems (MLSys)](https://mlsys.org/) and teaches the MIT Professional Education course on [Designing Efficient Deep Learning Systems](https://professional.mit.edu/programs/short-programs/designing-efficient-deep-learning-systems). 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 2018 Symposium on VLSI Circuits Best Student Paper Award, 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 Prof. Sze's research, please visit the Energy-Efficient Multimedia Systems Group [website](http://www.rle.mit.edu/eems/) __For research updates__: Follow [@eems_mit](https://twitter.com/eems_mit) on Twitter or sign up for the [EEMS mailing list](http://mailman.mit.edu/mailman/listinfo/eems-news )