US 12,450,466 B2
Superpixel methods for convolutional neural networks
Reginald Clifford Young, Palo Alto, CA (US); and Jonathan Ross, Mountain View, CA (US)
Assigned to Google LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on Oct. 1, 2020, as Appl. No. 17/060,420.
Application 17/060,420 is a continuation of application No. 16/717,341, filed on Dec. 17, 2019, granted, now 10,810,483.
Application 16/717,341 is a continuation of application No. 15/209,658, filed on Jul. 13, 2016, granted, now 10,706,348, issued on Jul. 7, 2020.
Prior Publication US 2021/0125029 A1, Apr. 29, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/04 (2023.01); G06F 17/16 (2006.01); G06N 3/02 (2006.01); G06N 3/06 (2006.01); G06N 3/063 (2023.01)
CPC G06N 3/04 (2013.01) [G06F 17/16 (2013.01); G06N 3/02 (2013.01); G06N 3/063 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method performed using a convolutional neural network implemented on a hardware integrated circuit, the method comprising:
receiving an input tensor for a layer of the convolutional neural network having a stride greater than one, the input tensor having multiple dimensions and a plurality of inputs;
generating a modified input tensor from the input tensor based on the stride greater than one, the modified input tensor having multiple dimensions and a respective plurality of inputs;
processing the modified input tensor using a modified weight matrix for a superpixel layer of the convolutional neural network having a stride equal to one, comprising:
applying a convolution to inputs of the modified input tensor using the modified weight matrix; and
in response to processing the modified input tensor, generating a transformed layer output of the superpixel layer, the transformed layer output comprising outputs that correspond mathematically to a neural network output generated by processing the input tensor using an unmodified version of the modified weight matrix;
wherein each of the input tensor and the modified input tensor are multi-dimensional tensors with a respective depth dimension; and
the respective depth dimension of the modified input tensor is greater than the respective depth dimension of the input tensor.