US 2025/0094780 A1Published application

Multi-resolution field representations in neural networks

AssigneeQUALCOMM Incorporated

InventorsKartikeya BHARDWAJ, Piero ZAPPI, Paul Nicholas WHATMOUGH, Christopher LOTT, Viswanath GANAPATHY, Chirag Sureshbhai PATEL, Joseph Binamira SORIAGA

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Abstract

Certain aspects provide techniques and apparatuses for efficiently processing inputs in a neural network using multiple receptive field sizes. An example method includes partitioning a first input into a first set of channels and a second set of channels. At a first layer of a neural network, the first set of channels and the second set of channels are convolved into a first output having a smaller dimensionality a dimensionality of the first input. The first set of channels and the first output are concatenated into a second input. The second input is convolved into a second output via a second layer of the neural network, wherein the second output merges a first receptive field generated by the first layer with a larger second receptive field generated by the second layer. One or more actions are taken based on at least one of the first output and the second output.

Claims30

Dependent claims

A processing system, comprising:

at least one memory having executable instructions stored thereon;
and
one or more processors communicatively coupled with the at least one memory and configured to execute the executable instructions in order to cause the processing system to:
partition a first input into a first set of channels and a second set of channels;
convolve, at a first layer of a neural network, the first set of channels and the second set of channels into a first output having a smaller dimensionality than a dimensionality of the first input;
concatenate the first set of channels and the first output into a second input for a second layer of the neural network;
convolve the second input into a second output via the second layer of the neural network, wherein:
the second output merges a first receptive field generated by the first layer of the neural network with a second receptive field generated by the second layer of the neural network, and
the first receptive field covers a larger receptive field in the first input than the second receptive field;
and
take one or more actions based on at least one of the first output and the second output.

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Dependencies are inferred from the claim text.