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1.发明授权公开(公告)号：US11468301B2
公开(公告)日：2022-10-11
申请号：US16203017
申请日：2018-11-28
发明人： Delin Li , Kun Ling , Liang Chen , Jianjun Li
摘要： Disclosed are a method and an apparatus for performing an operation of a convolutional layer in a convolutional neural network. The method includes reading unfolded-feature-data and an original convolution kernel from DRAM, padding the unfolded-feature-data, folding the padded unfolded-feature-data in at least one dimension folded feature data, storing the folded feature data into a SRAM, folding the original convolution kernel in the at least one dimension to generate one or more folded convolution kernels, storing the one or more folded convolution kernels in the SRAM and reading the folded feature data and the one or more folded convolution kernels from the SRAM into a calculation circuit for performing a convolution operation on the folded feature data by using the one or more folded convolution kernels.
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公开(公告)号：US11461632B2
公开(公告)日：2022-10-04
申请号：US15960119
申请日：2018-04-23
发明人： Kun Ling , Liang Chen , Jianjun Li , Delin Li , Chang Huang
摘要： Disclosed are a method and an apparatus for adapting parameters of a neural network. The method includes selecting one or more dimensions for a weight parameter of each of at least one layer of the neural network, determining a dimension value and a corresponding target value in each dimension of the weight parameter, and padding the weight parameter in a case where the dimension value in at least one dimension of the weight parameter is less than the corresponding target value, the dimension value in each dimension of the weight parameter after the padding being equal to the corresponding target value.
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公开(公告)号：US11822616B2
公开(公告)日：2023-11-21
申请号：US16203031
申请日：2018-11-28
发明人： Delin Li , Kun Ling , Liang Chen , Jianjun Li
摘要： Disclosed are a method and an apparatus for performing an operation of a convolutional layer in a convolutional neural network. The method comprises: reading unfolded feature data provided to the convolution layer and an original convolution kernel of the convolutional layer from a dynamic random access memory (DRAM); folding the unfolded feature data in at least one dimension of width and height to generate folded feature data; pre-processing the folded feature data and the original convolution kernel; storing the pre-processed folded feature data into a static random-access memory (SRAM); folding the pre-processed original convolution kernel in the at least one dimension to generate one or more folded convolution kernels corresponding to the original convolution kernel; storing the one or more folded convolution kernels in the SRAM; and reading the pre-processed folded feature data and the one or more folded convolution kernels from the SRAM into a calculation unit for convolving the pre-processed folded feature data with the one or more folded convolution kernels. By means of the method and/or apparatus in accordance with embodiments of the present disclosure, channel utilization may be improved, cache occupancy may be reduced, and operation efficiency may be improved.
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公开(公告)号：US11500958B2
公开(公告)日：2022-11-15
申请号：US16202991
申请日：2018-11-28
发明人： Delin Li , Kun Ling , Liang Chen , Jianjun Li
摘要： Disclosed are a method and an apparatus for performing convolution operation on folded feature data. The method comprises: reading the folded feature data provided to a convolution layer and an original convolution kernel from a dynamic random access memory (DRAM); pre-processing the folded feature data and the original convolution kernel; storing the pre-processed folded feature data into a static random-access memory (SRAM); folding the pre-processed original convolution kernel in at least one dimension of width or height according to a folding manner of the folded feature data to generate one or more folded convolution kernels corresponding to the original convolution kernel; storing the one or more folded convolution kernels in the SRAM; and reading the pre-processed folded feature data and the one or more folded convolution kernels from the SRAM into a calculation unit for convolving the pre-processed folded feature data with the one or more folded convolution kernels.
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公开(公告)号：US20190156185A1
公开(公告)日：2019-05-23
申请号：US16196063
申请日：2018-11-20
发明人： Jianjun Li , Chang Huang , Liang Chen , Kun Ling , Delin Li
IPC分类号： G06N3/04 , G06F12/0875 , G06F17/15
摘要： A method and an apparatus for adapting feature data in a convolutional neural network. The method includes selecting a plurality of consecutive layers; determining an expected number of subdata blocks and a layout position, width and height of each subdata block in an output feature data of a last layer; determining, for each current layer, a layout position, width, and height of each subdata block of an input feature data for the current layer according to the layout position, width, and height of each subdata block of the output feature data for the current layer; determining an actual position of each subdata block of the input feature data for a first layer in the input feature data for the first layer; and obtaining the expected number of subdata blocks of the input feature data for the first layer according to the actual position, width and height of each subdata block of the input feature data for the first layer.
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公开(公告)号：US20190130265A1
公开(公告)日：2019-05-02
申请号：US16170360
申请日：2018-10-25
发明人： Kun Ling , Chang Huang , Liang Chen , Delin Li , Jianjun Li , Feng Zhou
摘要： A method and apparatus for performing operations in a convolutional neural network. A method for performing operations in a convolutional neural network may include splitting a weight parameter of a selected layer in the convolutional neural network to obtain an operational parameter array including a plurality of operational parameters, performing operations in the selected layer by using each operational parameter in the operational parameter array to obtain a partial operational result array including a plurality of partial operational results, and generating one or more output data of the selected layer based on the partial operational result array. By this method, the convolutional neural network may achieve an improved execution efficiency.
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公开(公告)号：US12131182B2
公开(公告)日：2024-10-29
申请号：US16362281
申请日：2019-03-22
发明人： Zhenjiang Wang , Jianjun Li , Liang Chen , Kun Ling , Delin Li , Chen Sun
CPC分类号： G06F9/4881 , G06F9/30007 , G06F9/3004 , G06F9/30076 , G06F9/345 , G06F9/5016 , G06N3/063
摘要： Systems and methods of data processing are provided. The method comprises receiving an input data to be processed by a series of operations, identifying a first operation from the series of operations, selecting at least one second operation from the series of operations to be grouped with the first operation based at least in part on an amount of an input data and an output data of the grouped operations and the capacity of the memory unit, and processing a portion of the input data of the grouped operations. An efficiency of the series of data operations can be improved by ensuring the input data and output data of any data operations are both stored in the memory unit.
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公开(公告)号：US11574031B2
公开(公告)日：2023-02-07
申请号：US16222693
申请日：2018-12-17
发明人： Liang Chen , Chang Huang , Kun Ling , Jianjun Li , Delin Li , Heng Luo
摘要： Disclosed is a method for convolution calculation in a neural network, comprising: reading an input feature map, depthwise convolution kernels and pointwise convolution kernels from a dynamitic random access memory (DRAM); performing depthwise convolution calculations and pointwise convolution calculations according to the input feature map, the depthwise convolution kernels and the pointwise convolution kernels to obtain output feature values of a first predetermined number p of points on all pointwise convolution output channels; storing the output feature values of a first predetermined number p of points on all pointwise convolution output channels into an on-chip memory, wherein the first predetermined number p is determined according to at least one of available space in the on-chip memory, a number of the depthwise convolution calculation units, and width, height and channel dimensions of the input feature map; and repeating the above operation obtain output feature values of all points on all pointwise convolution output channels. Therefore, the storage space for storing intermediate results may be reduced.
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公开(公告)号：US11568216B2
公开(公告)日：2023-01-31
申请号：US16196063
申请日：2018-11-20
发明人： Jianjun Li , Chang Huang , Liang Chen , Kun Ling , Delin Li
IPC分类号： G06N3/04 , G06N3/08 , G06F12/0875 , G06F17/15
摘要： A method and an apparatus for adapting feature data in a convolutional neural network. The method includes selecting a plurality of consecutive layers; determining an expected number of subdata blocks and a layout position, width and height of each subdata block in an output feature data of a last layer; determining, for each current layer, a layout position, width, and height of each subdata block of an input feature data for the current layer according to the layout position, width, and height of each subdata block of the output feature data for the current layer; determining an actual position of each subdata block of the input feature data for a first layer in the input feature data for the first layer; and obtaining the expected number of subdata blocks of the input feature data for the first layer according to the actual position, width and height of each subdata block of the input feature data for the first layer.
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