Nanjing Horizon Robotics Technology Co., Ltd.

Beijing Horizon Information Technology Co., Ltd

Beijing Horizon Robotics Technology Research and Development Co., Ltd.

Kun Ling

Liang Chen

Jianjun Li

Chang Huang

Delin Li

Feng Zhou

Heng Luo

Chen Sun

Zhenjiang Wang

Honghe Tan

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 dynamic random access memory (DRAM); performing depthwise convolution calculations and pointwise convolution calculations by depthwise convolution calculation units and pointwise convolution calculation units, 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; and repeating above operation to obtain output feature values of all points on all point wise convolution output channels. Therefore, the storage space for storing intermediate results may be reduced.

Method, electronic device and storage medium for convolution calculation in neural network

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.

Convolutional neural network operation grouping based on memory unit capacity

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.

Method and apparatus for performing operation of convolutional layers in convolutional neural network

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.

Method and electronic device for convolution calculation in neural network

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.

Method and apparatus for adapting feature data in a convolutional neural network

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.

Method and apparatus for performing convolution operation on folded feature data

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.

Method and apparatus for performing operation of convolutional layer in convolutional neural network

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.

Method and apparatus for adapting parameters of neural network

Disclosed is an apparatus for performing a convolution operation in a convolutional neural network. The apparatus may comprise a selector for selecting one or more nonzero elements of a weight parameter, a selector for selecting a data item(s) corresponding to selected nonzero elements in input feature data, and a calculator unit for performing an operation. The apparatus may realize the convolution operation in a sparsified convolutional neural network efficiently through the hardware.

Apparatus for performing convolution operations in a convolutional neural network

A method for data management is provided. The method comprises: storing the plurality of items in a contiguous space within the memory, executing an instruction containing an address and a size that together identify the contiguous space to transmit the plurality of items from the main memory to a random-access memory (RAM) on a chip, and the chip includes a computing unit comprising a plurality of multipliers; and instructing the computing unit on the chip to: retrieve multiple of the plurality of items from the RAM; and perform a plurality of parallel operations using the plurality of multipliers with the multiple items to yield output data.

Systems and methods for data management

Disclosed are a method and an apparatus for accessing tensor data. The method may include determining a first row address in a first memory where one or more first data items to be accessed in a logical structure of the tensor data are stored at the first row address, copying data items at the first row address in the first memory to a first buffer row of a first buffer, moving each first data item in the first buffer row of the first buffer to a corresponding location at least in a first buffer row of a second buffer, and storing data items in the first buffer row of the second buffer into corresponding target locations in the second memory.

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Beijing, China

Kun Ling