Methods and apparatus relating to advanced graphics Power State management are described. In one embodiment, measurement logic detects information about idle transitions and active transitions of a power-well of a processor. In turn, determination logic determines performance loss and/or energy gain based at least in part on the detected information and power-on latency of the power-well of the processor. Other embodiments are also disclosed and claimed.

An LED controller system includes an LED controller including an image frame buffer able to receive image data. A sensor processing module is used to receive and process sensor data and a decision module is used to determine actions taken in response to processed sensor data. An image creation module is used to create images to be sent to the image frame buffer of the LED controller.

Disclosed are a dense optical flow calculation system and method based on an FPGA (Field Programmable Gate Array). The system comprises a software system deployed on a host and a dense optical flow calculation module deployed on the FPGA. Pixel information of two continuous frames of pictures is obtained from a host end in the system, and optical flow is obtained by calculation by means of the steps such as smoothing processing, polynomial expansion, intermediate variable calculation, optical flow calculation. An image pyramid and iterative optical flow calculation can be achieved by repeatedly calling a calculation core module in the FPGA; a final calculation result is returned to the host end. According to the dense optical flow calculation system in the present invention, methods of data flow, assembly line, separated convolution, block RAM array storage and the like are applied, the dense optical flow can be efficiently calculated, the dense optical flow result is high in reliability, the requirements of real-time processing and low power consumption can be met, and the practicability of the dense optical flow calculation system is guaranteed.

A method for analysis of yeast includes: receiving a microscopic image of yeast by a cloud server (2901), the microscopic image including a scaling pattern for determining a magnification; determining the magnification by the cloud server based on the scaling pattern (2902); and analyzing, by the cloud server, the microscopic image based on the magnification to obtain an analysis result (2903).

The described embodiments include systems, methods, and apparatuses for increased efficiency processing flow. One method includes a plurality of stages configured to process an execution graph that includes a plurality of logical nodes with defined properties and resources associated with each logical node of the plurality of logical nodes, a recirculating ring buffer, wherein the recirculating ring buffer is configured to holding only any one of a control information, input, and, or out data necessary to stream a temporary data between each logical node of the execution graph, and a data producer, wherein the data producer is configured to stall from writing control information into a command buffer upon the command buffer being full, preventing command buffer over-writing.

A method for correcting an image divides an output image into a grid with vertical sections of width smaller than the image width but wide enough to allow efficient bursts when writing distortion corrected line sections into memory. A distortion correction engine includes a relatively small amount of memory for an input image buffer but without requiring unduly complex control. The input image buffer accommodates enough lines of an input image to cover the distortion of a single most vertically distorted line section of the input image. The memory required for the input image buffer can be significantly less than would be required to store all the lines of a distorted input image spanning a maximal distortion of a complete line within the input image.

In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.

Systems and methods are disclosed for sensor prioritization for composite image capture. For example, methods may include selecting an image sensor as a prioritized sensor from among an array of two or more image sensors; determining one or more image processing parameters based on one or more images captured using the prioritized sensor; applying image processing using the one or more image processing parameters to images captured with each image sensor in the array of two or more image sensors to obtain respective processed images for the array of two or more image sensors; and stitching the respective processed images for the array of two or more image sensors to obtain a composite image.

A multiple-processor system for a multiple-lens camera is disclosed. The system comprises multiple processor components (PCs) and multiple links. Each PC comprises multiple I/O ports and a processing unit. The multiple-lens camera captures a X-degree horizontal field of view and a Y-degree vertical field of view, where X<=360 and Y<180. Each link connects one of the I/O ports of one of the PCs to one of the I/O ports of another one of the PCs such that each PC is connected by two or more respective links to one or two neighboring PCs. Each link is configured to transfer data in one direction.

A graphics processor architecture provides for scan conversion and ray tracing approaches to visible surface determination as concurrent and separate processes. Surfaces can be identified for shading by scan conversion and ray tracing. Data produced by each can be normalized, so that instances of shaders, being executed on a unified shading computation resource, can shade surfaces originating from both ray tracing and rasterization. Such resource also may execute geometry shaders. The shaders can emit rays to be tested for intersection by the ray tracing process. Such shaders can complete, without waiting for those emitted rays to complete. Where scan conversion operates on tiles of 2-D screen pixels, the ray tracing can be tile aware, and controlled to prioritize testing of rays based on scan conversion status. Ray population can be controlled by feedback to any of scan conversion, and shading.