Abstract: Deep neural networks use the computational power of massively parallel processors in applications such as autonomous driving. Autonomous driving demands resiliency (as in safety and reliability) and trillions of operations per second of computing performance to process sensor data with extreme accuracy. This keynote examines various approaches to achieve resiliency in autonomous cars and makes the case for design diversity based redundancy.
Biography: Nirmal R. Saxena is currently a distinguished engineer at NVIDIA and is responsible for HPC and automotive resilient computing. From 2011 through 2015, Nirmal was associated with Inphi Corp as CTO for Storage & Computing and with Samsung Electronics as Sr. Director working on fault-tolerant DRAM memory and storage array architectures. During 2006 through 2011, Nirmal held roles as a Principal Architect, Chief Server Hardware Architect & VP at NVIDIA. From 1991 through 2009, he was also associated with Stanford University’s Center for Reliable Computing and EE Department as Associate Director and Consulting Professor respectively. During his association with Stanford University, he taught courses in Logic Design, Computer Architecture, Fault-Tolerant Computing, supervised six PhD students and was co-investigator with Professor Edward J. McCluskey on DARPA’s ROAR (Reliability Obtained through Adaptive Reconfiguration) project. Nirmal held senior technical and management positions at Alliance Semiconductors, Chip Engines, Tiara Networks, Silicon Graphics, HaL Computers, and Hewlett Packard.
Nirmal received his Ph.D. EE degree (1991) from Stanford University. He is a Fellow of the IEEE (2002) and was cited for his contributions to reliable computing.