Practical Aspects of Machine Learning Circuits and Systems: Efficient Computing for AI and Robotics
Room: 1.518, Bldg: EER, 2501 Speedway Dr. , Austin, Texas, United States, 78712 , Virtual: https://events.vtools.ieee.org/m/440366Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design The compute demands of AI and robotics continue to rise due to the rapidly growing volume of data to be processed; the increasingly complex algorithms for higher quality of results; and the demands for energy efficiency and real-time performance. In this talk, we will discuss the design of efficient tailored hardware accelerators and the co-design of algorithms and hardware that reduce the energy consumption while delivering swift real-time and robust performance for applications including deep neural networks, data analytics with sparse tensor algebra, and autonomous navigation. Throughout the talk, we will highlight important design principles, methodologies, and tools that can facilitate an effective design process and various forms of co-design that can broaden the design space. Vivienne Sze is a Professor in the Electrical Engineering and Computer Science Department at MIT. She 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. Her work has been recognized by various awards, including faculty awards from Google, Facebook, and Qualcomm, the Symposium on VLSI Circuits Best Student Paper Award, the IEEE Custom Integrated Circuits Conference Outstanding Invited Paper Award, the IEEE Micro Top Picks Award and the International Symposium on Performance Analysis of Systems and Software Best Paper Award. As a member of the Joint Collaborative Team on Video Coding, she received the Primetime Engineering Emmy Award for the development of the High-Efficiency Video Coding video compression standard. She is a co-editor of High Efficiency Video Coding (HEVC): Algorithms and Architectures (Springer, 2014) and co-author of Efficient Processing of Deep Neural Networks (Synthesis Lectures on Computer Architecture, Morgan Claypool, 2020). For more information about Prof. Sze’s research, please visit (http://sze.mit.edu/). Co-sponsored by: University of Texas at Austin Speaker(s): Vivienne Sze, Room: 1.518, Bldg: EER, 2501 Speedway Dr. , Austin, Texas, United States, 78712 , Virtual: https://events.vtools.ieee.org/m/440366