Abstract: A monitoring system for a vehicle includes at least one imaging device configured to capture a first image type and a second image type in a sequence. A first illumination source is configured to emit a flood illumination captured by the at least one imaging device in the first image type. A second illumination source is configured to emit a structured light illumination captured by the at least one imaging device in the second image type. At least one processor is configured to extract a 2-dimensional (“2D”) joint coordinate representation of a vehicle occupant from the first image type, measure a depth of at least a portion of the 2D joint coordinate representation with the second image type, extrapolate a 3-dimensional (“3D”) joint coordinate representation of the vehicle occupant, and estimate, based on the 3D joint coordinate representation, a location of at least one joint that is obscured.

Abstract: Automatic multi-objective hardware optimization for processing a deep learning network is disclosed. An example of a storage medium includes instructions for obtaining client preferences for a plurality of performance indicators for processing of a deep learning workload; generating a workload representation for the deep learning workload; providing the workload representation to machine learning processing to generate a workload executable, the workload executable including hardware mapping based on the client preferences; and applying the workload executable in processing of the deep learning workload.

Abstract: Automatic multi-objective hardware optimization for processing a deep learning network is disclosed. An example of a storage medium includes instructions for obtaining client preferences for a plurality of performance indicators for processing of a deep learning workload; generating a workload representation for the deep learning workload; providing the workload representation to machine learning processing to generate a workload executable, the workload executable including hardware mapping based on the client preferences; and applying the workload executable in processing of the deep learning workload.