Advanced driver-assistance systems (ADAS) require low-latency and high-accuracy inference with an additional constraint of low-power performance that can only be achieved with custom designed hardware technologies. We present one such technology that distinguishes itself from traditional machine learning accelerators by utilizing an event-based processing architecture, low-bit computation, and an on-chip learning algorithm. In this talk we explain how our event-based, neuromorphic architecture enables efficient inference for person detection, face identification, keyword spotting, and LIDAR-based object detection applications that are critical for ADAS deployments.