Abstract: A hydrogen bond induced high stability porous covalent organic gel material and a preparation method thereof are provided. The method comprises: dissolving tetrakis(4-carboxyphenyl)silane in methanol to obtain solution A; adding concentrated sulfuric acid to the solution A, then water-bath heating and re-flowing to obtain a solution B; evaporating the solution B, dissolving remaining powder with an ethyl acetate, washing and drying, filtering and then evaporating the filtrate until crystallization to obtain a colorless solid C; dissolving the colorless solid C and hydrazine hydrate in methanol, water-bath re-fluxing, filtering and collecting white powder, washing and drying to obtain a white solid D; adding the white solid D and 1,4-Phthalaldehyde to N,N-dimethyl-formamide, adding trifluoroacetic acid, and then getting the desired material.

Abstract: To provide automated categorization of structured textual content individual nodes of textual content, from a document object model encapsulation of the structured textual content, have a multidimensional vector associated with them, where the values of the various dimensions of the multidimensional vector are based on the textual content in the corresponding node, the visual features applied or associated with the textual content of the corresponding node, and positional information of the textual content of the corresponding node. The multidimensional vectors are input to a neighbor-imbuing neural network. The enhanced multidimensional vectors output by the neighbor-imbuing neural network are then be provided to a categorization neural network. The resulting output can be in the form of multidimensional vectors whose dimensionality is proportional to categories into which the structured textual content is to be categorized. A weighted merge takes into account multiple nodes that are grouped together.

Abstract: The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.

Abstract: The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.

Abstract: The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.

Abstract: An embodiment of the present disclosure provides an operation apparatus which includes a storage unit, a control unit and a compute unit. The technical solution provided in this disclosure can reduce resource consumption of convolution operation, improve the speed of convolution operation and reduce operation time.

Abstract: The present disclosure provides a winograd convolution operation method, a winograd convolution operation apparatus, a device, and a storage medium. The apparatus includes: processors and a memory, where the memory is configured to store a program code, and the processors are configured to call the program code stored in the memory and execute the operation method. Through the operation method, a system, the device and the storage medium of the present disclosure, performance loss of a computer system may be reduced, and operation speed may be improved. Through the present disclosure, processing efficiency may be improved.

Abstract: This disclosure relates to a data processing method, a data processing apparatus, and related products. The products include a control unit. The control unit includes: an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is used for storing a calculation instruction associated with an artificial neural network computation; the instruction processing unit is used for parsing the calculation instruction to obtain a plurality of computation instructions; and the storage queue unit is used for storing an instruction queue, where the instruction queue includes the plurality of computation instructions or calculation instructions to be executed according to a front-back sequence of a queue. Through the above method of this disclosure, computation efficiency of the related products during a neural network model computation may be improved.

Abstract: The technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.

Abstract: A hydrogen bond induced high stability porous covalent organic gel material and a preparation method thereof are provided. The method comprises: dissolving tetrakis(4-carboxyphenyl)silane in methanol to obtain solution A; adding concentrated sulfuric acid to the solution A, then water-bath heating and re-flowing to obtain a solution B; evaporating the solution B, dissolving remaining powder with an ethyl acetate, washing and drying, filtering and then evaporating the filtrate until crystallization to obtain a colorless solid C; dissolving the colorless solid C and hydrazine hydrate in methanol, water-bath re-fluxing, filtering and collecting white powder, washing and drying to obtain a white solid D; adding the white solid D and 1,4-Phthalaldehyde to N,N-dimethyl-formamide, adding trifluoroacetic acid, and then getting the desired material.

Abstract: Embodiments of the present disclosure relate to a method and an apparatus for processing data, and related products. The embodiments of the present disclosure relate to a board card, which includes a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus respectively. The storage component is used to store data, the interface apparatus is used to realize data transmission between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card may be used to perform artificial intelligence computations.

Abstract: Embodiments of the present disclosure relate to a method and an apparatus for processing data, and related products. The embodiments of the present disclosure relate to a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip, where the artificial intelligence chip is connected to the storage component, the control component and the interface apparatus respectively. The storage component is used to store data; the interface apparatus is used to realize data transmission between the artificial intelligence chip and the external device. The control component is used to monitor a state of the artificial intelligence chip. The board card may be used to perform artificial intelligence computations.

Abstract: Embodiments of the present disclosure relate to a method and an apparatus for processing data, and related products. The embodiments of the present disclosure provide a board card including a storage component, an interface device, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface device, respectively; the storage component is configured to store data; the interface device is configured to implement data transfer between the artificial intelligence chip and external equipment; and the control component is configured to monitor a state of the artificial intelligence chip. The board card is configured to perform artificial intelligence operations.

Abstract: The present disclosure relates to a method, a device, and related products for processing data. In an embodiment of the present disclosure, when processing data related to a neural network, an optimal truncation threshold value for a plurality of pieces of data is determined. The data is truncated through the truncation data threshold, and the plurality of pieces of data is quantized from a high-precision format to a low-precision format. The method in the present disclosure can ensure the precision of data processing as high as possible while reducing the amount of data processing. In addition, the method also helps to significantly reduce the amount of data transmission, thereby greatly accelerating the data exchange among a plurality of computing devices.

Abstract: To provide automated categorization of structured textual content individual nodes of textual content, from a document object model encapsulation of the structured textual content, have a multidimensional vector associated with them, where the values of the various dimensions of the multidimensional vector are based on the textual content in the corresponding node, the visual features applied or associated with the textual content of the corresponding node, and positional information of the textual content of the corresponding node. The multidimensional vectors are input to a neighbor-imbuing neural network. The enhanced multidimensional vectors output by the neighbor-imbuing neural network are then be provided to a categorization neural network. The resulting output can be in the form of multidimensional vectors whose dimensionality is proportional to categories into which the structured textual content is to be categorized. A weighted merge takes into account multiple nodes that are grouped together.

Abstract: The present disclosure provides a data processing method, a board card device, a computer equipment, and a storage medium for data quantization. The board card provided in the present disclosure includes a storage component, an interface device, a control component, and an artificial intelligence chip of a data processing device, where the artificial intelligence chip is connected to the storage device, the control device, and the interface apparatus, respectively. According to the data processing method, the device, the computer equipment, and the storage medium provided in the embodiments of the present disclosure, data to be quantized is quantized according to a corresponding quantization parameter, which may reduce the storage space of data while ensuring the precision, as well as ensure the accuracy and reliability of the operation result and improve the operation efficiency.

Abstract: The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.

Abstract: The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.

Abstract: The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.