How can efficient, low-latency and high-accuracy inference be performed in ADAS?
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.
Kristofor Carlson received his PhD in Physics from Purdue University. Kristofor spent four years as a postdoctoral scholar at UC Irvine where he studied spiking neural networks, evolutionary algorithms, and neuromorphic systems. Afterwards, he...
Executive Vice-President, Brand Director, Embedded Franchise, OpenSystems Media
Richard Nass’ key responsibilities include setting the direction for all aspects of OpenSystems Media’s Embedded and IoT portfolios, including digital, print, and live events. Previously, Nass was the Brand Director or UBM’s award-winning Design...