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

Other

Nanjing Horizon Robotics Technology Co., Ltd.

Beijing Horizon Information Technology Co., Ltd

Zhenjiang Wang

Jianjun Li

Liang Chen

Chen Sun

Chang Huang

Kun Ling

Delin Li

Kun Hsien Lin

Meng Yao

Yu Zhou

Wenhui Cao

Zhuoran Zhao

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

This application discloses a data processing method and apparatus, an electronic device, and a storage medium. When execution is performed at an operation layer of a neural network model, based on a pre-stored buffer allocation relationship, a first address range for cyclic addressing is set for a first buffer corresponding to input data and a second address range for cyclic addressing is set for a second buffer corresponding to an output result. Subsequently, cyclic addressing can be performed in the first buffer based on the first address range for cyclic addressing, to read the input data for the operation layer; and cyclic addressing can be performed in the second buffer based on the second address range for cyclic addressing, to write the output result of the operation layer into the second buffer. In this way, efficiency of buffer utilization can be effectively improved, and further operation efficiency for the model is improved.

Method and electronic device for data processing, and storage medium

Disclosed are a data compression method, a computer-readable storage medium, and an electronic device. The method includes: converting each data in a to-be-compressed data set into binary data in a preset format; determining a to-be-compressed bit and a significant bit for the each data in the to-be-compressed data set based on a sequence of all bits of the binary data; determining a compression bit width corresponding to the to-be-compressed data set based on bit widths of the significant bits; compressing the each data in the to-be-compressed data set based on the compression bit width, to obtain a compressed data set; and generating attribute information of the compressed data set. According to the present disclosure, the significant bit can be determined based on the sequence of all bits without adjusting orders of the bits of the binary data, thereby simplifying a data compression process and improving efficiency of data compression.

Data compression method and apparatus, computer-readable storage medium, and electronic device

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.

Method and apparatus for accessing tensor data

A method and apparatus are disclosed to perform the circular addressing to emulate a virtually unlimited memory space despite the fixed capacity of a physical memory by readdressing the portion of the data that exceeds the pre-defined length of the circular addressing region to another pre-defined address in the circular addressing region. Data segments in a data sample can be loaded and computed with recalculated circular addresses for different applications.

Methods and apparatus for using circular addressing in convolutional operation

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Beijing, China

Zhenjiang Wang