Until recently, the majority of sensor-based AI processing used vision and speech inputs. Recently, we have begun to see radar, LiDAR, event-based image sensors, and other types of sensors used in new AI applications. And, increasingly, system developers are incorporating multiple, heterogeneous sensors in their designs and utilizing sensor fusion techniques to enable more robust machine perception. In this presentation, we’ll explore some of the heterogeneous sensor combinations and sensor fusion approaches that are gaining adoption in applications such as driver assistance and mobile robots. These sensor fusion techniques are using a mix of AI techniques and traditional DSP processing. We’ll also show how the Cadence Tensilica ConnX DSP, Vision DSP, and AI Accelerator IP families and their associated software tools and libraries support sensor fusion applications with high performance, efficiency, and ease of development.