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.

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.