Convolution with kernel expansion and tensor accumulation
AssigneeQUALCOMM Incorporated
InventorsJamie Menjay Lin, Yash Sanjay Bhalgat, Edwin Chongwoo Park
Life of this patent
Estimating the term…
Abstract
Certain aspects of the present disclosure provide techniques for kernel expansion. An input data tensor is received at a first layer in a neural network, and a first convolution is performed for a first kernel, where the first kernel has a size greater than a preferred size. Performing the first convolution comprises generating a plurality of intermediate tensors by performing a plurality of intermediate convolutions using a plurality of intermediate kernels with a size of the preferred size, and accumulating the plurality of intermediate tensors to generate an output tensor for the first convolution.
Claims30
A computer-implemented method of machine learning, comprising:
- receiving, at a first layer in a neural network, an input data tensor;
- and
- performing a first convolution for a first kernel in the first layer in the neural network, wherein the first kernel has a size greater than a preferred size, comprising:
- generating a plurality of intermediate tensors by performing a plurality of intermediate convolutions using the input data tensor and a plurality of intermediate kernels with a size of the preferred size, wherein an intermediate kernel of the plurality of intermediate kernels comprises a portion of the first kernel and has the same number of dimensions as the first kernel;
- and
- accumulating the plurality of intermediate tensors to generate an output tensor for the first convolution.
A processing system comprising:
- at least one memory comprising computer-executable instructions;
- and
- one or more processors configured to execute the computer-executable instructions and cause the processing system to perform an operation comprising:
- receiving, at a first layer in a neural network, an input data tensor;
- and
- performing a first convolution for a first kernel in the first layer in the neural network, wherein the first kernel has a size greater than a preferred size, comprising:
- generating a plurality of intermediate tensors by performing a plurality of intermediate convolutions using the input data tensor and a plurality of intermediate kernels with a size of the preferred size, wherein an intermediate kernel of the plurality of intermediate kernels comprises a portion of the first kernel and has the same number of dimensions as the first kernel;
- and
- accumulating the plurality of intermediate tensors to generate an output tensor for the first convolution.
A non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising:
- receiving, at a first layer in a neural network, an input data tensor;
- and
- performing a first convolution for a first kernel in the first layer in the neural network, wherein the first kernel has a size greater than a preferred size, comprising:
- generating a plurality of intermediate tensors by performing a plurality of intermediate convolutions using the input data tensor and a plurality of intermediate kernels with a size of the preferred size, wherein an intermediate kernel of the plurality of intermediate kernels comprises a portion of the first kernel and has the same number of dimensions as the first kernel;
- and
- accumulating the plurality of intermediate tensors to generate an output tensor for the first convolution.
A processing system comprising:
- means for receiving, at a layer in a neural network, an input data tensor;
- and
- means for performing a convolution for a first kernel in the layer in the neural network, wherein the first kernel has a size greater than a preferred size, the means for performing comprising:
- means for generating a plurality of intermediate tensors by performing a plurality of intermediate convolutions using the input data tensor and a plurality of intermediate kernels with a size of the preferred size, wherein an intermediate kernel of the plurality of intermediate kernels comprises a portion of the first kernel and has the same number of dimensions as the first kernel;
- and
- means for accumulating the plurality of intermediate tensors to generate an output tensor for the convolution.
Dependencies are inferred from the claim text.