Abstract: Disclosed techniques relate to forming a block sum of picture elements employing a vector dot product instruction to sum packed picture elements and the mask producing a vector of masked horizontal picture element. The block sum is formed from plural horizontal sums via vector single instruction multiple data (SIMD) addition.

Abstract: Disclosed techniques relate to forming a block sum of picture elements employing a vector dot product instruction to sum packed picture elements and the mask producing a vector of masked horizontal picture element. The block sum is formed from plural horizontal sums via vector single instruction multiple data (SIMD) addition.

Abstract: This disclosure is directed to the problem of paralleling random read access within a reasonably sized block of data for a vector SIMD processor. The invention sets up plural parallel look up tables, moves data from main memory to each plural parallel look up table and then employs a look up table read instruction to simultaneously move data from each parallel look up table to a corresponding part a vector destination register. This enables data processing by vector single instruction multiple data (SIMD) operations. This vector destination register load can be repeated if the tables store more used data. New data can be loaded into the original tables if appropriate. A level one memory is preferably partitioned as part data cache and part directly addressable memory. The look up table memory is stored in the directly addressable memory.

Abstract: Systems and methods for matrix-solve applications include a memory-optimized hardware acceleration (HWA) solution with scalable architecture (i.e. specialized circuitry) for HWA matrix-solve operations. The matrix-solve solutions described herein may include a scalable hardware architecture with parallel processing (e.g., “within column” processing), which provides the ability to compute several output values in parallel. The HWA matrix-solve solutions described herein may include simultaneous multi-column processing, which provides a lower execution cycle count and a reduced total number of memory accesses. This HWA matrix-solve provides a low latency and energy-efficient matrix-solve solutions, which may be used to reduce energy consumption and improve performance in various matrix-based applications, such as computer vision, SLAM, AR/VR/mixed-reality, machine learning, data analytics, and other matrix-based applications.

Abstract: One embodiment provides an image processing circuitry. The image processing circuitry includes a feature extraction circuitry and an optimization circuitry. The feature extraction circuitry is to determine a feature descriptor based, at least in part, on a feature point location and a corresponding scale. The optimization circuitry is to optimize an operation of the feature extraction circuitry. Each optimization is to at least one of accelerate the operation of the feature extraction circuitry, reduce a power consumption of the feature extraction circuitry and/or reduce a system memory bandwidth used by the feature extraction circuitry.

Abstract: An example apparatus for selecting keypoints in image includes a keypoint detector to detect keypoints in a plurality of received images. The apparatus also includes a score calculator to calculate a keypoint score for each of the detected keypoints based on a descriptor score indicating descriptor invariance. The apparatus includes a keypoint selector to select keypoints based on the calculated keypoint scores. The apparatus also further includes a descriptor calculator to calculate descriptors for each of the selected keypoints. The apparatus also includes a descriptor matcher to match corresponding descriptors between images in the plurality of received images. The apparatus further also includes a feature tracker to track a feature in the plurality of images based on the matched descriptors.

Abstract: An example apparatus for tracking features in image data includes an image data receiver to receive initial image data corresponding to an image from a camera and store the image data a circular buffer. The apparatus also includes a feature detector to detect features in the image data. The apparatus further includes a feature sorter to sort the detected features to generate sorted feature points. The apparatus includes a feature tracker to track the sorted feature points in subsequent image data corresponding to the image received at the image data receiver. The subsequent image data is to replace the initial image data in the circular buffer.

Abstract: Systems and methods for matrix-solve applications include a memory-optimized hardware acceleration (HWA) solution with scalable architecture (i.e. specialized circuitry) for HWA matrix-solve operations. The matrix-solve solutions described herein may include a scalable hardware architecture with parallel processing (e.g., “within column” processing), which provides the ability to compute several output values in parallel. The HWA matrix-solve solutions described herein may include simultaneous multi-column processing, which provides a lower execution cycle count and a reduced total number of memory accesses. This HWA matrix-solve provides a low latency and energy-efficient matrix-solve solutions, which may be used to reduce energy consumption and improve performance in various matrix-based applications, such as computer vision, SLAM, AR/VR/mixed-reality, machine learning, data analytics, and other matrix-based applications.

Abstract: Systems and methods for a hardware accelerated matrix decomposition matrix decomposition circuit are described herein. This matrix decomposition circuit splits matrix decomposition operations into parallel operation circuits and serial operation circuits, and joins the parallel and serial operation circuits using specific dependency handling logic for efficient parallel execution. This provides fast matrix decomposition with low power consumption, reduced memory footprint, and reduced memory bandwidth.

Abstract: One embodiment provides an image processing circuitry. The image processing circuitry includes a feature extraction circuitry and an optimization circuitry. The feature extraction circuitry is to determine a feature descriptor based, at least in part, on a feature point location and a corresponding scale. The optimization circuitry is to optimize an operation of the feature extraction circuitry. Each optimization is to at least one of accelerate the operation of the feature extraction circuitry, reduce a power consumption of the feature extraction circuitry and/or reduce a system memory bandwidth used by the feature extraction circuitry.

Abstract: This disclosure is directed to the problem of paralleling random read access within a reasonably sized block of data for a vector SIMD processor. The invention sets up plural parallel look up tables, moves data from main memory to each plural parallel look up table and then employs a look up table read instruction to simultaneously move data from each parallel look up table to a corresponding part a vector destination register. This enables data processing by vector single instruction multiple data (SIMD) operations. This vector destination register load can be repeated if the tables store more used data. New data can be loaded into the original tables if appropriate. A level one memory is preferably partitioned as part data cache and part directly addressable memory. The look up table memory is stored in the directly addressable memory.

Abstract: Aspects of the present disclosure relates to technologies (systems, devices, methods, etc.) for performing feature detection and/or feature tracking based on image data. In embodiments, the technologies include or leverage a SLAM hardware accelerator (SWA) that includes a feature detection component and optionally a feature tracking component. The feature detection component may be configured to perform feature detection on working data encompassed by a sliding window. The feature tracking component is configured to perform feature tracking operations to track one or more detected features, e.g., using normalized cross correlation (NCC) or another method.

Abstract: A low power video hardware engine is disclosed. The video hardware engine includes a video hardware accelerator unit. A shared memory is coupled to the video hardware accelerator unit, and a scrambler is coupled to the shared memory. A vDMA (video direct memory access) engine is coupled to the scrambler, and an external memory is coupled to the vDMA engine. The scrambler receives an LCU (largest coding unit) from the vDMA engine. The LCU comprises N×N pixels, and the scrambler scrambles N×N pixels in the LCU to generate a plurality of blocks with M×M pixels. N and M are integers and M is less than N.

Abstract: A method is disclosed for efficiently calculating a median value of a high-order array in a Single Instruction Multiple Data (SIMD) processor. Values of the high-order array are sorted vertically in each column followed by sorts on each individual row. After the sort, selective diagonal values of the sorted high-order array are used to form a low-order array to calculate the median of the high-order array. The median calculation using selective diagonal values of the high-order array in a low-order array significantly improves SIMD processor efficiency and throughput.

Abstract: A video hardware engine which support dynamic frame padding is disclosed. The video hardware engine includes an external memory. The external memory stores a reference frame. The reference frame includes a plurality of reference pixels. A motion estimation (ME) engine receives a current LCU (largest coding unit), and defines a search area around the current LCU for motion estimation. The ME engine receives a set of reference pixels corresponding to the current LCU. The set of reference pixels of the plurality of reference pixels are received from the external memory. The ME engine pads a set of duplicate pixels along an edge of the reference frame when a part area of the search area is outside the reference frame.

Abstract: An electronic tracing process includes packing both stall (215) and reason (219) data into a single high priority timing information stream. An integrated circuit includes an electronic processor (110), and a tracing circuit (120) operable to pack both stall and events data into a single timing information stream. Other circuits, processes and systems are also disclosed.

Abstract: An image processing system includes a processor and optical flow determination logic. The optical flow determination logic is to quantify relative motion of a feature present in a first frame of video and a second frame of video with respect to the two frames of video. The optical flow determination logic configures the processor to convert each of the frames of video into a hierarchical image pyramid. The image pyramid comprises a plurality of image levels. Image resolution is reduced at each higher one of the image levels. For each image level and for each pixel in the first frame, the processor is configured to establish an initial estimate of a location of the pixel in the second frame and to apply a plurality of sequential searches, starting from the initial estimate, that establish refined estimates of the location of the pixel in the second frame.

Abstract: This invention enables effective corner detection of pixels of an image using the FAST algorithm using a vector SIMD processor. This invention loads an 8×8 pixel block that includes four 7×7 pixel blocks including the 16 peripheral pixels to be tested for each of four center pixels. This invention rearranges the 64 pixels of the 8×8 block to form a 16 element array for each center pixel preferably using a vector permutation instruction. This invention uses vector SIMD subtraction and compare and vector SIMD addition and compare to make the FAST algorithm comparisons. The N consecutive pixels determinations of the FAST algorithm are made from the results of plural shift and AND operations. The corresponding center pixel is marked a corner or not a corner dependent upon of the results of plural shift and AND operations.

Abstract: This invention enables effective corner detection of pixels of an image using the FAST algorithm using a vector SIMD processor. This invention loads an 8×8 pixel block that includes four 7×7 pixel blocks including the 16 peripheral pixels to be tested for each of four center pixels. This invention rearranges the 64 pixels of the 8×8 block to form a 16 element array for each center pixel preferably using a vector permutation instruction. This invention uses vector SIMD subtraction and compare and vector SIMD addition and compare to make the FAST algorithm comparisons. The N consecutive pixels determinations of the FAST algorithm are made from the results of plural shift and AND operations. The corresponding center pixel is marked a corner or not a corner dependent upon of the results of plural shift and AND operations.

Abstract: A method is disclosed for efficiently calculating a median value of a high-order array in a Single Instruction Multiple Data (SIMD) processor. Values of the high-order array are sorted vertically in each column followed by sorts on each individual row. After the sort, selective diagonal values of the sorted high-order array are used to form a low-order array to calculate the median of the high-order array. The median calculation using selective diagonal values of the high-order array in a low-order array significantly improves SIMD processor efficiency and throughput.