Cambricon Technologies Corporation Limited

Shanghai Cambricon Information Technology Co., Ltd

Cambricon (xi'an) Semiconductor Co., Ltd.

Chinese Academy of Sciences

Sanghai Cambricon Information Technology Co., Ltd.

Tianshi Chen

Shaoli Liu

Yunji Chen

Qi Guo

Shuai Hu

Zai Wang

Tian Zhi

Zidong Du

Xiao Lin Zhang

Yao Zhang

Bingrui Wang

Shengyuan Zhou

Dong Han

Jinhua Tao

Zhen Li

Yifan Hao

Tao Luo

Xuda Zhou

Yimin Zhuang

Xiaobing Chen

Shijin Zhang

Yuzhe Luo

Lei Zhang

Xinkai Song

Cheng Qian

Daofu Liu

Wei Li

Yong Yu

Xunyu Chen

Huiying Lan

Jie Wei

Xiaobin Chen

Yufeng Gao

Zhou Fang

Xishan Zhang

Yixuan Ren

Haoyuan He

Shangying Li

Aspects for forward propagation of a convolutional artificial 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 a convolution kernel with one of the one or more portions of the input data to generate a slave output value. Further still, the aspects may include an interconnection unit configured to combine the one or more slave output values into one or more intermediate result vectors, wherein the master computation module is further configured to merge the one or more intermediate result vectors into a merged intermediate vector.

Apparatus and methods for forward propagation in convolutional neural networks

Aspects for forward propagation of a multilayer neural network (MNN) in a neural network processor are described herein. As an example, the aspects may include a computation module that includes a master computation module and one or more slave computation modules. The master computation module may be configured to receive one or more groups of MNN data. The one or more groups of MNN data may include input data and one or more weight values and wherein at least a portion of the input data and the weight values are stored as discrete values. The one or more slave computation modules may be configured to calculate one or more groups of slave output values based on a data type of each of the one or more groups of MNN data.

Apparatus and methods for forward propagation in neural networks supporting discrete data

Aspects for neural network operations with fixed-point number of short bit length are described herein. The aspects may include a fixed-point number converter configured to convert one or more first floating-point numbers to one or more first fixed-point numbers in accordance with at least one format. Further, the aspects may include a neural network processor configured to process the first fixed-point numbers to generate one or more process results.

Apparatus and methods for neural network operations supporting fixed point numbers of short bit length

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.

Apparatus and methods for training in convolutional neural networks

The application provides a processor and processing method. The processor includes a task segmentation device configured to perform task segmentation according to a task segmentation granularity and a hardware resource division device configured to divide hardware resources of the processor according to a task segmentation result. The processor and processing method provided by the application improve the processing performance and reduce the overhead by performing task segmentation and configuring different hardware according to task segmentation.

Data sharing system and data sharing method therefor

Aspects for backpropagation in a fully connect layer of a convolutional neural network are described herein. The aspects may include a direct memory access unit configured to receive input data and one or more first data gradients from a storage device. The aspects may further include a master computation module configured to transmit the input data and the one or more first data gradients to one or more slave computation modules. The slave computation modules are respectively configured to multiply one of the one or more first data gradients with the input data to generate a default weight gradient vector.

Apparatus and methods for training in fully connected layers of convolutional networks

Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device, calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information, and outputting the second information. By means of the examples in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency. The present technical solution has advantages of a fast computation speed and high efficiency.

Information processing method and system

Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device; calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information; and outputting the second information. By means of the examples in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency. The present technical solution has advantages of a fast computation speed and high efficiency.

Information processing method and terminal device

An information processing method applied to a computation circuit is disclosed. The computation circuit includes a communication circuit and an operation circuit. The method includes controlling, by the computation circuit, the communication circuit to obtain a voice to be identified input by a user; controlling, by the computation circuit, the operation circuit to obtain and call an operation instruction to perform voice identification processing on the voice to be identified to obtain target text information corresponding to the voice to be identified. The operation instruction is a preset instruction for voice identification.

Voice recognition based on neural networks

Aspects for backpropagation of a multilayer neural network (MNN) in a neural network processor are described herein. The aspects may include a computation module configured to receive one or more groups of MNN data. The computation module may further include a master computation module configured to calculate an input gradient vector based on a first output gradient vector from an adjacent layer and based on a data type of each of the one or more groups of MNN data. Further still, the computation module may include one or more slave computation modules configured to parallelly calculate portions of a second output vector based on the input gradient vector calculated by the master computation module and based on the data type of each of the one or more groups of MNN data.

Apparatus and methods for backward propagation in neural networks supporting discrete data

The present disclosure relates to a video retrieval method, a method, system and device for generating a video retrieval mapping relationship, and a storage medium. The video retrieval method comprises: acquiring a retrieval instruction, wherein the retrieval instruction carries retrieval information for retrieving a target frame picture; and obtaining the target frame picture according to the retrieval information and a preset mapping relationship. The method for generating a video retrieval mapping relationship comprises: performing a feature extraction operation on each frame picture in a video stream by using a feature extraction model so as to obtain a key feature sequence corresponding to each frame picture; inputting the key feature sequence corresponding to each frame picture into a text sequence extraction model for processing so as to obtain a text description sequence corresponding to each frame picture; and constructing a mapping relationship according to the text description sequence corresponding to each frame picture. By means of the video retrieval method and the method for generating a video retrieval mapping relationship provided in the present application, the efficiency of video retrieval can be improved, and human-machine interaction is made more intelligent.

Video retrieval method, and method and apparatus for generating video retrieval mapping relationship

Image retouching method and terminal device

The present disclosure provides a computing method that is applied to a computing device. The computing device includes: a memory, a register unit, and a matrix computing unit. The method includes the following steps: controlling, by the computing device, the matrix computing unit to obtain a first operation instruction, where the first operation instruction includes a matrix reading instruction for a matrix required for executing the instruction; controlling, by the computing device, an operating unit to send a reading command to the memory according to the matrix reading instruction; and controlling, by the computing device, the operating unit to read a matrix corresponding to the matrix reading instruction in a batch reading manner, and executing the first operation instruction on the matrix. The technical solutions in the present disclosure have the advantages of fast computing speed and high efficiency.

Calculation method and related product

Growing community of inventors

Beijing, China

Tianshi Chen