Keynote 1: The road to resilient computing in autonomous driving is paved with redundancy

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.