Abstract

Deep learning techniques for embedded vision processors are critical in the push toward fully autonomous vehicles. Processors must quickly interpret content and context from sensors and cameras and react in real-time. To accomplish this in an energy-efficient way, embedded vision processors often employ heterogenous, multicore architectures that are optimized for the variety of processing tasks that enable the car to “see” and “interpret” its environment. In addition to meeting stringent performance and power requirements, automotive semiconductors responsible for critical driving tasks must also be certified to the highest levels of automotive safety. This presentation will discuss processor IP designed specifically to meet the current and next-generation requirements of ADAS vision applications, including Automotive Safety Integrity Level (ASIL) D classification as defined in the ISO 26262 Functional Safety specification.

Biography

Dr. Yankin Tanurhan, Vice President Engineering, DesignWare Processor Cores, IP Subsystems, Non-Volatile Memory at Synopsys leading low power and high performance ARC and EV embedded Processor developments targeted from Mobile, IoT, Embedded Vision, Digital Home, Automotive/Industrial, Security to Storage markets, ASIP tool development with products like ASIP Designer and Programmer, IP Subsystems products like Sensor Fusion, Audio, Vision and Security Subsystems and CMOS based Non Volatile IP development.

Dr Tanurhan has authored 100+ papers in refereed publications. He holds a B.S. and M.S. in Electrical and Computer Engineering from Rheinisch Westfaellische Technische Hochschule (RWTH) in Aachen, Germany and a Dr. Ing. degree summa cum laude in Electrical Engineering from the University of Karlsruhe (TH) in Karlsruhe, Germany.