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    IMAGE SIGNAL PROCESSING
    1.
    发明申请
    IMAGE SIGNAL PROCESSING 有权

    公开(公告)号：US20250005719A1

    公开(公告)日：2025-01-02

    申请号：US18757196

    申请日：2024-06-27

    Applicant: Imagination Technologies Limited

    Inventor： James Imber

    IPC: G06T5/60

    Abstract: A differentiable module of a differentiable model of an image signal processor having a pipeline of functional blocks, wherein the differentiable module is configured to implement a single functional block of the pipeline, the differentiable module including base logic configured to receive an input image signal and to process the received input image signal by performing a base image processing function that represents a task of the functional block of the pipeline implemented by the module; a refinement function configured to receive the input image signal and to process the received input image signal in parallel to the processing of the received input image signal by the base logic; and combining logic configured to combine the processed image signal from the base logic and the processed image signal from the refinement function to determine an output image signal to be outputted from the differentiable module. A corresponding method is also described.

    END-TO-END DATA FORMAT SELECTION FOR HARDWARE IMPLEMENTATION OF DEEP NEURAL NETWORK
    2.
    发明公开
    END-TO-END DATA FORMAT SELECTION FOR HARDWARE IMPLEMENTATION OF DEEP NEURAL NETWORK 审中-公开

    公开(公告)号：US20240346314A1

    公开(公告)日：2024-10-17

    申请号：US18752216

    申请日：2024-06-24

    Applicant: Imagination Technologies Limited

    Inventor： James Imber

    IPC: G06N3/08 ,  G06F7/483 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063 ,  G06N3/082

    CPC classification number: G06N3/08 ,  G06F7/483 ,  G06N3/04 ,  G06N3/063 ,  G06N3/082 ,  G06N3/045

    Abstract: Methods for selecting fixed point number formats for representing values input to and/or output from layers of a Deep Neural Network (DNN) which take into account the impact of the fixed point number formats for a particular layer in the DNN. The fixed point number format(s) used to represent sets of values input to and/or output from a layer are selected one layer at a time in a predetermined sequence wherein any layer is preceded in the sequence by the layer(s) from which it depends. The fixed point number format(s) for each layer is/are selected based on the error in the output of the DNN associated with the fixed point number formats. Once the fixed point number format(s) for a layer has/have been selected any calculation of the error in the output of the DNN for a subsequent layer in the sequence is based on that layer being configured to use the selected fixed point number formats.

    Methods and systems for implementing a convolution transpose layer of a neural network
    4.
    发明授权
    Methods and systems for implementing a convolution transpose layer of a neural network 有权

    公开(公告)号：US11886536B2

    公开(公告)日：2024-01-30

    申请号：US18096521

    申请日：2023-01-12

    Applicant: Imagination Technologies Limited

    Inventor： Cagatay Dikici ,  Clifford Gibson ,  James Imber

    IPC: G06F17/15 ,  G06N3/063

    CPC classification number: G06F17/153 ,  G06N3/063

    Abstract: Methods and systems for performing a convolution transpose operation between an input tensor having a plurality of input elements and a filter comprising a plurality of filter weights. The method includes: dividing the filter into a plurality of sub-filters; performing, using hardware logic, a convolution operation between the input tensor and each of the plurality of sub-filters to generate a plurality of sub-output tensors, each sub-output tensor comprising a plurality of output elements; and interleaving, using hardware logic, the output elements of the plurality of sub-output tensors to form a final output tensor for the convolution transpose.

    Error allocation format selection for hardware implementation of deep neural network
    5.
    发明授权
    Error allocation format selection for hardware implementation of deep neural network 有权

    公开(公告)号：US11734553B2

    公开(公告)日：2023-08-22

    申请号：US17846803

    申请日：2022-06-22

    Applicant: Imagination Technologies Limited

    Inventor： James Imber

    IPC: G06F11/00 ,  G06N3/063 ,  G06N3/084 ,  G06F7/483 ,  G06F17/11 ,  G06N3/047 ,  G06N3/048

    CPC classification number: G06N3/063 ,  G06F7/4836 ,  G06F17/11 ,  G06N3/084 ,  G06N3/047 ,  G06N3/048

    Abstract: Methods for determining a fixed point format for one or more layers of a DNN based on the portion of the output error of the DNN attributed to the fixed point formats of the different layers. Specifically, in the methods described herein the output error of a DNN attributable to the quantisation of the weights or input data values of each layer is determined using a Taylor approximation and the fixed point number format of one or more layers is adjusted based on the attribution. For example, where the fixed point number formats used by a DNN comprises an exponent and a mantissa bit length, the mantissa bit length of the layer allocated the lowest portion of the output error may be reduced, or the mantissa bit length of the layer allocated the highest portion of the output error may be increased. Such a method may be iteratively repeated to determine an optimum set of fixed point number formats for the layers of a DNN.

    Implementing Traditional Computer Vision Algorithms as Neural Networks
    8.
    发明申请
    Implementing Traditional Computer Vision Algorithms as Neural Networks 审中-公开

    公开(公告)号：US20190354844A1

    公开(公告)日：2019-11-21

    申请号：US16418322

    申请日：2019-05-21

    Applicant: Imagination Technologies Limited

    Inventor： Paul Brasnett ,  Daniel Valdez Balderas ,  Cagatay Dikici ,  Szabolcs Cséfalvay ,  David Hough ,  Timothy Smith ,  James Imber

    IPC: G06N3/063 ,  G06N3/08 ,  G06F17/16 ,  G06F17/15

    Abstract: Methods and systems for implementing a traditional computer vision algorithm as a neural network. The method includes: receiving a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations; mapping each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation; linking the one or more network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm; and configuring hardware logic capable of implementing a neural network to implement the neural network that represents the traditional computer vision algorithm.

    Systems and methods for processing images of objects using coarse intrinsic colour estimates
    9.
    发明授权
    Systems and methods for processing images of objects using coarse intrinsic colour estimates 有权

    公开(公告)号：US10181183B2

    公开(公告)日：2019-01-15

    申请号：US15333354

    申请日：2016-10-25

    Applicant: Imagination Technologies Limited

    Inventor： James Imber ,  Adrian Hilton ,  Jean-Yves Guillemaut

    IPC: G06T5/20 ,  G06K9/46 ,  G06T7/90

    Abstract: An image processing system and method for determining an intrinsic color component of one or more objects for use in rendering the object(s) is described herein. One or more input images are received, each representing a view of the object(s), wherein values of each of the input image(s) are separable into intrinsic color estimates and corresponding shading estimates. A depth image represents depths of the object(s). Coarse intrinsic color estimates are determined using the input image(s). The intrinsic color component is determined by applying bilateral filtering to the coarse intrinsic color estimates using bilateral filtering guidance terms based on depth values derived from the depth image.

    Systems and methods for processing images of objects using global lighting estimates
    10.
    发明授权
    Systems and methods for processing images of objects using global lighting estimates 有权

    公开(公告)号：US09959636B2

    公开(公告)日：2018-05-01

    申请号：US15333475

    申请日：2016-10-25

    Applicant: Imagination Technologies Limited

    Inventor： James Imber ,  Adrian Hilton ,  Jean-Yves Guillemaut

    IPC: G06K9/34 ,  G06T7/90 ,  G06T15/50

    CPC classification number: G06T7/90 ,  G06T15/506 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20028

    Abstract: An image processing system and method for determining an intrinsic colour component of one or more objects for use in rendering the object(s) is described. One or more input images are received, each representing a view of the object(s), wherein values of the input image(s) are separable into intrinsic colour estimates and corresponding shading estimates. A set of surface normals for the object(s) of the input image(s) is determined. In accordance with the values of the input image(s) and the determined set of surface normals, a global lighting estimate is determined which provides consistent corresponding intrinsic colour estimates for a plurality of regions of the object(s) from the input image(s). The intrinsic colour component is determined in accordance with the values of the input image(s) and the determined global lighting estimate. The determined intrinsic colour component of the object(s) and the determined set of surface normals for the object(s) are stored for use in rendering the object(s).

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