Inspur Suzhou Intelligent Technology Co., Ltd

Suzhou Metabrain Intelligent Technology Co., Ltd.

Ruizhen Wu

Lin Wang

Jingjing Chen

Ping Huang

Yongxing Zhang

Jian Cui

Jiang Wang

Xingyuan Li

Shuqing Li

Fang Wang

Xingang Ju

Wentao Liu

Xinwu Shen

Wendao Mu

Jiangbo Xu

Tangzhi Feng

Hongbin Yu

Mingming Wang

Peng Feng

A cache management method, apparatus and system, a device, and a nonvolatile readable storage medium. The method includes: receiving, by a storage system cache manager, a target operation request sent by a target user; processing the target operation request in a pipeline manner, so as to execute a processing operation corresponding to the target operation request on a cache line using the target operation request; and feeding back a processing result of the target operation request to the target user, and executing a cache maintenance operation corresponding to the processing result according to the processing result by means of a software cache manager.

Cache management method, apparatus and system, device and medium

A test method and a multi-processor SOC chip are provided. The method includes: parsing a first command line in a host system input buffer by a host system to obtain a first command and a first parameter corresponding to the first command line, when the first command is a command of the host system in a host system command set, executing the first command by the host system, and when the first command is a command corresponding to a subsystem, sending, by the host system, the first parameter to a subsystem input buffer corresponding to the subsystem as a second command line; and parsing the second command line by a subsystem to obtain a second command and a second parameter corresponding to the second command line, and when the second command is a command in a subsystem command set of the subsystem, executing the second command by the subsystem.

Test method and multi-processor SOC chip

A request processing method, includes: acquiring, from a local first input first output (FIFO) queue, an address of a current request to be processed, and based on the address of the current request to be processed, acquiring the current request to be processed from a preset request buffer area; parsing the current request to be processed, and based on a parsing result, acquiring corresponding current source data to be processed and a serial address item for recording an address of a next request to be processed; processing the current source data to be processed to obtain currently processed target data, and writing the currently processed target data into a preset data buffer area; and notifying a host to read all processed target data from the preset data buffer area when the serial address item is empty.

Request processing method and apparatus, device, and medium

A method and apparatus for generating checking data, an electronic device and a non-volatile computer-readable storage medium. The method includes: acquiring a store-status matrix corresponding to a redundant array of independent disks; determining non-reference vectors among the magnetic-disk vectors, and performing data-vector-element evenly dividing processing to an initial data region formed by the non-reference vectors, to obtain first and second data regions; performing data-exchange processing to the first and second data regions in the store-status matrix, to obtain a first matrix; determining a plurality of second-order matrixes in the first and second data regions of the first matrix, and performing position-exchange processing to target magnetic-disk elements in the second-order matrixes, to obtain a second matrix; based on the second matrix, performing single-error-correction encoding generating processing, to obtain first checking data; and based on the second matrix, performing double-error-correction encoding generating processing, to obtain second checking data.

Check data generation method and apparatus, device, and storage medium

A video image interference detection method includes the following steps: acquiring a background image and a current frame image; constructing an edge image of the background image and an edge image of the current frame image respectively; intersecting the edge image of the background image and the edge image of the current frame image to obtain a same boundary image between the edge image of the background image and the edge image of the current frame image; counting a first pixel number in the edge image of the background image and a second pixel number in the same edge image; determining whether the current frame image is interfered according to a ratio of the first pixel number to the second pixel number.

Video image interference detection method, system, device and medium

The present disclosure discloses a method and a system for performing check recovery based on Gaussian elimination, a apparatus, and a storage medium. The method includes: a disk array by using normal data blocks and normal check blocks is constructed, a first reference matrix constructed by using coding matrix rows corresponding to the normal data blocks and the normal check blocks is converted into an inverse matrix through Gaussian elimination; it is determined that coding matrix rows corresponding to a predetermined quantity of blocks, and a second reference matrix is constructed by using the coding matrix rows, the abnormal data blocks, and the abnormal check blocks; and the inverse matrix and the second reference matrix are multiplied to obtain a first matrix, the first matrix and the disk array are multiplied to obtain an output matrix, and original data are obtained according to the output matrix.

Method and system for performing check recovery based on Gaussian elimination, device, and medium

A method of image recognition includes: acquiring an image training sample data set, where the image training sample data set includes image training sample data and tag information corresponding to the image training sample data; constructing basic activation function and preset bias adjustment function as preset activation function in an addition relationship, determining the preset activation function to be an activation function of a neural network model, to obtain an initial neural network model, where the preset bias adjustment function is a function constructed from a symbol function, a first trainable parameter and a quadratic term in a multiplication relationship; inputting the image training sample data set into the initial neural network model for training until the model converges, to obtain a trained neural network model; when an image to be recognized is acquired, outputting a recognition result corresponding to the image by using the trained neural network model.

Image recognition method and apparatus, and device and medium

Disclosed are a data processing method and apparatus based on an optical neural network, a computer-readable storage medium, and an optical neural network. The method includes: acquiring initial optical information and final output optical information as well as intermediate input optical information and intermediate output optical information at input/output ports of the phase shifter of an input optical signal in a case that beam-splitting ratios of the beam splitters of the two interference optical path structure satisfy a beam-splitting compensation condition; calculating parameters of the internal phase shifters of the two interference optical path structures in a case that the initial optical information and the intermediate input optical information as well as the intermediate output optical information and the final output optical information satisfy a preset beam-splitting condition of the optical neural network; and performing data processing by using the optical neural network based on the parameters.

Optical neural network, data processing method and apparatus based on same, and storage medium

A method for managing a shared memory, including: setting a master core, applying, by the master core, to a system for idle hugepage blocks, mapping the idle hugepage blocks to a virtual address space of the master core, and sorting the hugepage blocks in an ascending order of physical address size of the hugepage blocks; dividing, depending on whether the physical addresses are continuous, the hugepage blocks sorted into segments; in response to determining that there are segments satisfying the memory demand of the subsystem, sorting the segments satisfying the memory demand of the subsystem in a descending order of lengths thereof, and remapping the sorted segments to obtain segments having both continuous virtual addresses and continuous real addresses; and in response to determine that there is a segment satisfying the memory demand of the system, releasing hugepage blocks other than the segment satisfying the memory demand of the system.

Shared memory management method and device

A method for realizing a Hadamard product, a device and a storage medium, includes: acquiring a plurality of to-be-treated optical signals with unequal wavelengths; inputting the to-be-treated optical signals into a wavelength division multiplexer; by using the wavelength division multiplexer, feeding the to-be-treated optical signals to a micro-ring-resonator component, wherein the micro-ring-resonator component includes a plurality of micro-ring-resonator groups each of which is formed by two micro-ring resonators with equal radii; and applying a corresponding electric current to the micro-ring-resonator component, to obtain a result of the Hadamard product according to an outputted light intensity.

Hadamard product implementation method and device, and storage medium

A connection analysis method for a multi-port nested model and a medium. The method includes: acquiring instance information and nested relationships of a multi-port nested model, and building an instance relationship tree; reading port information and connection information of instances, and adding the port information and the connection information to the instance relationship tree; acquiring the port information and the connection information of the instances of each node layer by layer according to the instance relationship tree to build a connection dictionary; and acquiring port-to-port connection information of the instances by retrieving the connection dictionary to perform connection analysis on the multi-port nested model.

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Jiangsu, China

Ruizhen Wu