公开(公告)号：US20250094780A1

公开(公告)日：2025-03-20

申请号：US18468203

申请日：2023-09-15

Applicant: QUALCOMM Incorporated

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

IPC: G06N3/0464

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.

公开(公告)号：US20250077951A1

公开(公告)日：2025-03-06

申请号：US18458786

申请日：2023-08-30

Applicant: QUALCOMM Incorporated

Inventor： Paul Nicholas WHATMOUGH

IPC: G06N20/00

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning model compression. A set of parameters for a machine learning model is accessed, where the set of parameters are formatted according to a first encoding. A converted set of parameters is generated based on applying a conversion operation to format the set of parameters according to a second encoding. A set of bit planes is generated based on applying a bit plane transformation to the converted set of parameters, and a compressed set of parameters for the machine learning model is generated based on applying a bit mask operation to one or more bit planes of the set of bit planes.