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 module 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 modules 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 module is further configured to update a prestored convolution kernel based on the kernel gradient.
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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
30 Claims
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1. An apparatus for backpropagation of a convolutional neural network, comprising:
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a master computation module 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 modules 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 module is further configured to update a prestored convolution kernel based on the kernel gradient. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A method for backpropagation of a convolutional neural network, comprising:
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receiving, by a direct memory access unit, input data from a storage device; selecting, by a master computation module, 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 modules, 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; and updating, by the master computation module, a prestored convolution kernel based on the kernel gradient. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
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Specification