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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.

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Hear from:

kristofer carlson

Kristofer Carlson
Manager of Applied Research

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