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Brain Behind Autonomous Driving Cars -- Multi-model Analytics and Embedded Processing

Wednesday, February 22, 2017, 12:00 PM
Nedderman Hall Room 105

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Brain Behind Autonomous Driving Cars -- Multi-model Analytics and Embedded Processing

Rama Venkatasubramanian, Ph.D.
Texas Instruments, DSP Systems group

Wednesday, February 22
Nedderman Hall Room 105


Computer vision, RADAR and LIDAR are key technologies that enable self-driving cars. Various analytics and algorithms are run real-time in the background to enable cars to operate themselves. These analytics and computing are carried out by one or more advanced driver assistance systems application processors. Embedded processors that provide analytics processing are key building blocks for processing image data, audio, video, and other sensor data. More recently, signal processing and data analytics have taken center stage with the proliferation of internet of things and machine learning. This talk will give an overview of the evolution of processing behind self-driving cars and highlight the recent advances in the field. It will also cover some of the recent advances in the quest for energy efficiency in signal processing in architecture, algorithms, and systems.


Rama Venkatasubramanian earned a B.E. degree in electrical and electronics engineering from the National Institute of Technology in Trichy, India, and M.S. and Ph.D. degrees from UT Dallas. Since 2005, he has been associated with the DSP Systems group at Texas Instruments, where he leads the DSP Development Team and is a senior member of technical staff. His research interests include DSP processor design, cache architectures and energy efficient circuit design using emerging technologies. He served as chair for the IEEE Dallas CAS chapter during 2014-15 and has served on the technical committee of DCAS, VLSI, and VDAT conferences. He has authored or co-authored two book

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