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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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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
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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公开(公告)号:US20240005129A1
公开(公告)日:2024-01-04
申请号:US18029849
申请日:2021-10-01
Applicant: Google LLC
Inventor: Yanqi Zhou , Amir Yazdanbakhsh , Berkin Akin , Daiyi Peng , Yuxiong Zhu , Mingxing Tan , Xuanyi Dong
IPC: G06N3/045 , G06N3/092 , G06N3/063 , G06N3/044 , G06N3/0464
CPC classification number: G06N3/045 , G06N3/092 , G06N3/0464 , G06N3/044 , G06N3/063
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for jointly determining neural network architectures and hardware accelerator architectures. In one aspect, a method includes: generating, using a controller policy, a batch of one or more output sequences, each output sequence in the batch defining a respective architecture of a child neural network and a respective architecture of a hardware accelerator; for each output sequence in the batch: training a respective instance of the child neural network having the architecture defined by the output sequence; evaluating a network performance of the trained instance of the child neural; and evaluating an accelerator performance of a respective instance of the hardware accelerator having the architecture defined by the output sequence to determine an accelerator performance metric for the instance of the hardware accelerator; and using the network performance metrics and the accelerator performance metrics to adjust the controller policy.
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