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    DYNAMICALLY DIVIDING ACTIVATIONS AND KERNELS FOR IMPROVING MEMORY EFFICIENCY
    2.
    发明申请
    DYNAMICALLY DIVIDING ACTIVATIONS AND KERNELS FOR IMPROVING MEMORY EFFICIENCY 有权

    公开(公告)号：US20210142179A1

    公开(公告)日：2021-05-13

    申请号：US17090071

    申请日：2020-11-05

    申请人： Intel Corporation

    发明人： Xiaoming Chen ,  Anbang Yao ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo

    IPC分类号： G06N3/08 ,  G06N3/04 ,  G06N3/063 ,  G06N20/10

    摘要： Embodiments are generally directed to dynamically dividing activations and kernels for improving memory efficiency. An embodiment of a method in a compute engine performing machine learning comprises: receiving, by a convolutional layer of a convolutional neural network (CNN) implemented on the compute engine, a plurality of activation groups contained in an input data, wherein the convolutional layer includes one or more kernel groups and the one or more kernel groups each include a plurality of kernels; determining a plurality of memory efficiency metrics based on the number of activation groups of the plurality of activation groups and the number of kernels of the plurality of kernels; selecting a first optimal number of activation groups and a second optimal number of kernels that are associated with an optimal memory efficiency metric in the plurality of memory efficiency metrics; and performing a convolutional operation on the input data based on the first optimal number and the second optimal number.

    METHODS AND APPARATUS TO PROCESS A MACHINE LEARNING MODEL IN A WEB-BROWSER ENVIRONMENT
    3.
    发明申请
    METHODS AND APPARATUS TO PROCESS A MACHINE LEARNING MODEL IN A WEB-BROWSER ENVIRONMENT 有权

    公开(公告)号：US20220253488A1

    公开(公告)日：2022-08-11

    申请号：US17630461

    申请日：2019-09-27

    申请人： Intel Corporation

    发明人： Jianhui Li ,  Yong Wu ,  Ningxin Hu ,  Yiqiang Li ,  Yuanke Luo

    IPC分类号： G06F16/954 ,  G06N3/04 ,  G06N3/08

    摘要： Methods, apparatus, systems, and articles of manufacture to process a machine learning model in a web-browser environment are disclosed. An example apparatus includes a graph builder to accumulate machine learning operations as a graph. A tensor manager is to, in response to a request to access a tensor that is not yet available and associated with the machine learning operations, identify the graph based on the tensor. A graph cache manager is to determine whether a condensed graph corresponding to the identified graph is available. A graph condenser is to, in response to the graph cache manager determining that the condensed graph is not available, generate the condensed graph. A graph executor is to execute the condensed graph to create the tensor. The tensor manager is to provide the tensor as a response to the request to access the tensor.

    ADAPTIVE DEFORMABLE KERNEL PREDICTION NETWORK FOR IMAGE DE-NOISING
    5.
    发明申请
    ADAPTIVE DEFORMABLE KERNEL PREDICTION NETWORK FOR IMAGE DE-NOISING 有权

    公开(公告)号：US20210142448A1

    公开(公告)日：2021-05-13

    申请号：US17090170

    申请日：2020-11-05

    申请人： Intel Corporation

    发明人： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Xiaoming Chen ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo ,  Yi Yang ,  Feng Chen ,  Zhiming Wang ,  Zhiqiao Zheng ,  Shandong Wang

    IPC分类号： G06T5/00 ,  G06N3/04

    摘要： Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.