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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.

    NEURAL NETWORK ARCHITECTURE FOR IMPLEMENTING GROUP CONVOLUTIONS
    3.
    发明申请
    NEURAL NETWORK ARCHITECTURE FOR IMPLEMENTING GROUP CONVOLUTIONS 有权

    公开(公告)号：US20250124700A1

    公开(公告)日：2025-04-17

    申请号：US18694626

    申请日：2021-10-08

    Applicant: Google LLC

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

    IPC: G06V10/82 ,  G06V10/77 ,  G06V10/94

    Abstract: Methods, systems, and apparatus, including computer-readable media, are described for processing an input image using a convolutional neural network (CNN). The CNN includes a sequence of layer blocks. Each of a first subset of the layer blocks in the sequence is configured to perform operations that include: i) receiving an input feature map for the layer block, ii) generating an expanded feature map from the input feature map using a group convolution, and iii) generating a reduced feature map from the expanded feature map. The input feature map is an h w feature map with c1 channels. The expanded feature map is an h w feature map with c2 channels, whereas the reduced feature map is an h w feature map with c1 channels. C2 is greater than c1. An output feature map is generated for the layer block from the reduced feature map.

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