Apparatus and methods for training in convolutional neural networks
First Claim
1. An apparatus for backpropagation of a convolutional neural network, comprising:
- a master computation circuit configured to;
receive input data, andselect one or more portions of the input data based on a predetermined convolution window in response to an instruction; and
one or more slave computation circuits respectively configured to convolute one of the one or more portions of the input data with one of one or more calculated first data gradients to generate a kernel gradient,wherein the master computation circuit is further configured to update a prestored convolution kernel based on the kernel gradient,wherein the one or more slave computation circuits are respectively configured to multiply at least a portion of the prestored convolution kernel with the one or more first data gradients, andwherein the master computation circuit is further configured to calculate one or more second data gradients based on a derivative of an activation function and a sum of one or more multiplication results between the first data gradients and the portion of the prestored convolution kernel.
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Abstract
Aspects for backpropagation of a convolutional neural network are described herein. The aspects may include a direct memory access unit configured to receive input data from a storage device and a master computation module configured to select one or more portions of the input data based on a predetermined convolution window. Further, the aspects may include one or more slave computation modules respectively configured to convolute one of the one or more portions of the input data with one of one or more previously calculated first data gradients to generate a kernel gradient, wherein the master computation module is further configured to update a prestored convolution kernel based on the kernel gradient.
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Citations
27 Claims
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1. An apparatus for backpropagation of a convolutional neural network, comprising:
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a master computation circuit configured to; receive input data, and select one or more portions of the input data based on a predetermined convolution window in response to an instruction; and one or more slave computation circuits respectively configured to convolute one of the one or more portions of the input data with one of one or more calculated first data gradients to generate a kernel gradient, wherein the master computation circuit is further configured to update a prestored convolution kernel based on the kernel gradient, wherein the one or more slave computation circuits are respectively configured to multiply at least a portion of the prestored convolution kernel with the one or more first data gradients, and wherein the master computation circuit is further configured to calculate one or more second data gradients based on a derivative of an activation function and a sum of one or more multiplication results between the first data gradients and the portion of the prestored convolution kernel. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for backpropagation of a convolutional neural network, comprising:
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receiving, by a direct memory access circuit, input data from a storage device; selecting, by a master computation circuit, one or more portions of the input data based on a predetermined convolution window in response to an instruction; convoluting, by one or more slave computation circuits, one of the one or more portions of the input data with one of one or more previously calculated first data gradients to generate a kernel gradient; updating, by the master computation circuit, a prestored convolution kernel based on the kernel gradient, multiplying, by the one or more slave computation circuits, at least a portion of the prestored convolution kernel with the one or more first data gradients; calculating, by the master computation circuit, one or more second data gradients based on a derivative of an activation function and a sum of one or more multiplication results between the first data gradients and the portion of the prestored convolution kernel. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
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Specification