Cambricon Technologies Corporation Limited

Boe Technology Group Co., Ltd.

Chinese Academy of Sciences

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Legend (beijing) Limited

Dong Han

Yunji Chen

Shaoli Liu

Tianshi Chen

Qi Guo

Xiang Sean Zhou

Xiao Lin Zhang

Dong Guo

Tao Luo

Shijin Zhang

Danfeng Zhang

Xiao Zhang

Zhixia Liao

Changqing Wan

Minghe Qiao

A method of configuring a cloud server resource, an electronic device and a storage medium, which specifically relate to a field of artificial intelligence technology, in particular to a field of cloud computing technology. A method of configuring a cloud server resource includes: obtaining an operation index of a server instance of an object; comparing the obtained operation index with a threshold; and determining whether a configuration of the cloud server resource is to be adjusted, based on a result of the comparing.

Method of configuring a cloud server resource, electronic device and storage medium

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 maxout layer operations in neural network are described herein. The aspects may include a load/store unit configured to retrieve input data from a storage module. The input data may be formatted as a three-dimensional vector that includes one or more feature values stored in a feature dimension of the three-dimensional vector. The aspects may further include a pruning unit configured to divide the one or more feature values into one or more feature groups based on one or more data ranges and select a maximum feature value from each of the one or more feature groups. Further still, the pruning unit may be configured to delete, in each of the one or more feature groups, feature values other than the maximum feature value and update the input data with the one or more maximum feature values.

Apparatus for operations at maxout layer of neural networks

Aspects for performing neural network operations are described herein. The aspects may include a first neural network processing module configured to process at least a portion of neural network data and an on-chip interconnection module communicatively connected to the first neural network processing module and one or more second neural network processing modules. The on-chip interconnection module may include a first layer interconnection module configured to communicate with an external storage device and one or more second layer interconnection modules respectively configured to communicate with the first neural network processing module and the one or more second neural network processing modules. Further, the first neural network processing module may include a neural network processor configured to perform one or more operations on the portion of the neural network data and a high-speed storage device configured to store results of the one or more operations.

Device and method for executing neural network operation

An example device comprises a central node for receiving vector data returned by leaf nodes, a plurality of leaf nodes for calculating and shifting the vector data, and forwarder modules comprising a local cache structure and a data processing component, wherein the plurality of leaf nodes are divided into N groups, each group having the same number of leaf nodes; the central node is individually in communication connection with each group of leaf nodes by means of the forwarder modules; a communication structure constituted by each group of leaf nodes has self-similarity; the plurality of leaf nodes are in communication connection with the central node in a complete M-way tree approach by means of the forwarder modules of multiple levels; each of the leaf nodes comprises a setting bit.

Device for vector data returning processing unit in fractal tree, method, control device, and intelligent chip

Aspects for vector comparison in neural network are described herein. The aspects may include a direct memory access unit configured to receive a first vector and a second vector from a storage device. The first vector may include one or more first elements and the second vector may include one or more second elements. The aspects may further include a computation module that includes one or more comparers respectively configured to generate a comparison result by comparing one of the one or more first elements to a corresponding one of the one or more second elements in accordance with an instruction.

Apparatus and methods for comparing vectors

The present disclosure provides a data read-write scheduler and a reservation station for vector operations. The data read-write scheduler suspends the instruction execution by providing a read instruction cache module and a write instruction cache module and detecting conflict instructions based on the two modules. After the time is satisfied, instructions are re-executed, thereby solving the read-after-write conflict and the write-after-read conflict between instructions and guaranteeing that correct data are provided to a vector operations component. Therefore, the subject disclosure has more values for promotion and application.

Data read-write scheduler and reservation station for vector operations

The present disclosure discloses a levelness detecting device and method, and a levelness adjusting device and method. The levelness detecting device comprises a light emitter, a semi-reflecting, semi-transmitting mirror, and a plurality of reflecting mirrors comprising a first reflecting mirror, a second reflecting mirror and a third reflecting mirror. The semi-reflecting, semi-transmitting mirror and the plurality of reflecting mirrors are arranged on a to-be-detected plane and are each provided with a scale indicative of a height.

Levelness detecting device and method, levelness adjusting device and method

A method of backing up and recovering data in the hard disk of a computer, comprising a step of backing up data in the hard disk, including: allocating a mirror partition in a hard disk for backing up the data in a partition of the hard disk where the data need to be backed up are stored; backing up integrally the data in the backed up partition specified by the user into the allocated corresponding mirror partition; and hiding the space of the mirror partition; the method further comprising a recovery step for recovering integrally the hard disk data in the mirror partition into the corresponding backed up partition according to the instruction of the user. The present invention can back up and recovery all kinds of data in the hard disk safely and reliably.

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Beijing, China

Dong Han