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    CONVOLUTIONAL NEURAL NETWORKS WITH SOFT KERNEL SELECTION
    1.
    发明申请
    CONVOLUTIONAL NEURAL NETWORKS WITH SOFT KERNEL SELECTION 有权

    公开(公告)号：US20220129740A1

    公开(公告)日：2022-04-28

    申请号：US17425283

    申请日：2020-01-23

    Applicant: Google LLC

    Inventor： Brandon Chauloon Yang ,  Quoc V. Le ,  Jiquan Ngiam ,  Gabriel Mintzer Bender

    IPC: G06N3/063

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using neural networks that include one or more conditional convolutional layers. A conditional convolutional layer has a plurality of kernels and determines a respective input-dependent weight for each of the plurality of kernels and generates an input-dependent kernel by computing a weighted sum of the plurality of kernels in accordance with the respective input-dependent weights.

    HARDWARE ACCELERATOR OPTIMIZED GROUP CONVOLUTION BASED NEURAL NETWORK MODELS
    2.
    发明申请
    HARDWARE ACCELERATOR OPTIMIZED GROUP CONVOLUTION BASED NEURAL NETWORK MODELS 有权

    公开(公告)号：US20240386260A1

    公开(公告)日：2024-11-21

    申请号：US18693724

    申请日：2021-10-08

    Applicant: Google LLC

    Inventor： Berkin Akin ,  Suyog Gupta ,  Cao Gao ,  Ping Zhou ,  Gabriel Mintzer Bender ,  Hanxiao Liu

    IPC: G06N3/063 ,  G06N3/0464 ,  G06V10/77 ,  G06V10/82 ,  G06V10/94

    Abstract: Methods, systems, and apparatus, including computer-readable media, are described for processing an input image using integrated circuit that implements a convolutional neural network with a group convolution layer. The processing includes determining a mapping of partitions along a channel dimension of an input feature map to multiply accumulate cells (MACs) in a computational unit of the circuit and applying a group convolution to the input feature map. Applying the group convolution includes, for each partition: providing weights for the group convolution layer to a subset of MACs based on the mapping; providing, via an input bus of the circuit, an input of the feature map to each MAC in the subset; and computing, at each MAC in the subset, a product using the input and a weight for the group convolution layer. An output feature map is generated for the group convolution layer based on an accumulation of products.

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