System and method for cascaded dynamic max pooling in neural networks
OwnerSerdar Sozubek, John Josephinventors
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Abstract
A method for performing size K×K max pooling with stride S at a max pooling layer of a convolutional neural network to downsample input data includes receiving input data, buffering the input data, applying a cascade of size 2×2 max pooling stages to the buffered input data to generate downsampled output data, wherein a stride value of each size 2×2 max pooling stage is determined dynamically in accordance with pooling parameters associated with the size 2×2 max pooling stage.
Claims20
A computer-implemented method for performing size K×K max pooling with stride S at a max pooling layer of a convolutional neural network to downsample input data, the computer-implemented method comprising:
- receiving, at the max pooling layer, input data;
- buffering, at the max pooling layer, the input data;
- applying, at the max pooling layer, a cascade of size 2×2 max pooling stages to the buffered input data to generate downsampled output data, wherein a stride value of each size 2×2 max pooling stage is determined dynamically in accordance with pooling parameters associated with the size 2×2 max pooling stage;
- and
- outputting, by the max pooling layer, the downsampled output data to another layer of the convolution neural network for further processing.
A device for performing size K×K max pooling with stride S at a max pooling layer of a convolutional neural network to downsample input data, the device comprising:
- a non-transitory memory storage comprising instructions;
- and
- one or more processors in communication with the memory storage, wherein the one or more processors execute the instructions to:
- receive input data,
- buffer the input data,
- apply a cascade of size 2×2 max pooling stages to the buffered input data to generate downsampled output data, wherein a stride value of each size 2×2 max pooling stage is determined dynamically in accordance with pooling parameters associated with the size 2×2 max pooling stage, and
- output the downsampled output data to another layer of the convolution neural network for further processing.
A non-transitory computer-readable media storing computer instructions for performing size K×K max pooling with stride S at a max pooling layer of a convolutional neural network to downsample input data, that when executed by one or more processors, cause the one or more processors to perform the steps of:
- receive input data,
- buffer the input data,
- apply a cascade of size 2×2 max pooling stages to the buffered input data to generate downsampled output data, wherein a stride value of each size 2×2 max pooling stage is determined dynamically in accordance with pooling parameters associated with the size 2×2 max pooling stage, and
- output the downsampled output data to another layer of the convolution neural network for further processing.
Dependencies are inferred from the claim text.