Abstract: Systems and methods to dynamically adjust operating conditions of a graphics processing unit (GPU) are disclosed. A machine learning model is trained to determine operating voltages and frequencies to be provided to a GPU core of the GPU to execute a workload comprising a plurality of commands. The trained machine learning model is deployed to firmware of the GPU. A command in the workload to be executed by the GPU core is received. The trained machine learning model determines operating voltage and frequency for the GPU core to execute the command.

Abstract: Systems and methods to dynamically adjust operating conditions of a graphics processing unit (GPU) are disclosed. A machine learning model is trained to determine operating voltages and frequencies to be provided to a GPU core of the GPU to execute a workload comprising a plurality of commands. The trained machine learning model is deployed to firmware of the GPU. A command in the workload to be executed by the GPU core is received. The trained machine learning model determines operating voltage and frequency for the GPU core to execute the command.

Abstract: A system and method are disclosed for processing image data. An example method includes sequentially receiving a plurality of raster lines corresponding to an image, and grouping the plurality of raster lines into a plurality of full-scale horizontal stripes of image data. For each full-scale horizontal stripe of image data, the method: generates a first downscaled version of the full-scale horizontal stripe, generates a full-scale rotated stripe by rotating the full-scale horizontal stripe to a vertical orientation, generates a first downscaled rotated stripe by rotating the first downscaled version of the full-scale horizontal stripe to the vertical orientation, and performs image processing on the full-scale rotated stripe and the first downscaled rotated stripe before all subsequent raster lines of the image have been received.

Abstract: Systems and methods for switching voltage operating points of an image signal processing pipeline are provided. The method comprises capturing an image during a sensor frame time. The method further comprises storing a plurality of pixels associated with the captured image. The method further comprises reading a first set of plurality of pixels within the sensor frame time. The method further comprises processing the first set of plurality of pixels and count a first set of plurality of pixels. The method further comprises comparing the count value with a pixel threshold value. The method further comprises, in response to the pixel count value reaching the pixel threshold value, switching the image signal processing pipeline from a first voltage operating point to a second voltage operating point.

Abstract: Methods and apparatus improve static region detection in an imaging pipeline. An imaging pipeline may perform detection of static regions of an image at multiple stages of the pipeline. For example, as static regions may be eliminated from further processing by the imaging pipeline, static region detection performed at an early stage of the pipeline may provide for maximized power savings. As images early in the pipeline may contain artifacts inhibiting detection of some static regions, additional static region detection may be performed after further image processing. For example, static region detection may be performed for a second time after some filtering is applied to images in the pipeline. Regions previously characterized as dynamic may be characterized as static later in the pipeline due to a reduction of noise for example provided by the filters, and differences between the static region detection at different positions within the imaging pipeline.