Abstract: The present disclosure relates to a data quantization processing method and apparatus, an electronic device, and a storage medium. The apparatus includes a control unit having an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store a calculation instruction associated with an artificial neural network operation, the instruction processing unit is configured to parse the calculation instruction to obtain a plurality of operation instructions, and the storage queue unit is configured to store an instruction queue. The instruction queue includes a plurality of operation instructions or calculation instructions to be executed in an order of the queue. The above-mentioned method improves the operation precision of related products during a neural network model operation.

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: A method for adjusting quantization parameters of a recurrent neural network according to an embodiment of the present disclosure may determine a target iteration interval according to the data variation range of the data to be quantized to adjust quantization parameters in the recurrent neural network computation according to the target iteration interval. The quantization parameter adjustment method, apparatus, and related products of the recurrent neural network of the present disclosure may improve the quantization precision, efficiency, and computation efficiency of the recurrent neural network.

Abstract: The present disclosure provides a computing device for processing a multi-bit width value, an integrated circuit board card, a method, and a computer readable storage medium. The computing device is included in the combined processing apparatus, and the combined processing apparatus further includes a general interconnection interface, and other processing devices. The computing device interacts with the other processing device to jointly complete a computing operation specified by a user. The combined processing apparatus further includes a storage device connected to an apparatus and the other processing devices and configured to store data of the apparatus and the other processing device. The solution of the present disclosure can split the multi-bit width value so that the processing capability of the processor is not influenced by the bit width.

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: The present disclosure relates to a computing device for processing a multi-bit width value, an integrated circuit board card, a method, and a computer readable storage medium. The computing device may be included in a combined processing apparatus, and the combined processing apparatus may further include a general interconnection interface, and an other processing device. The computing device interacts with the other processing device to jointly complete a computing operation specified by a user. The combined processing apparatus may further include a storage device connected to an apparatus and the other processing device and configured to store data of the apparatus and the other processing device. The solution of the present disclosure can split the multi-bit width value so that the processing capability of the processor is not influenced by the bit width.

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

Abstract: The present disclosure provides a data processing method, a data processing device, a computer equipment, and a storage medium. The data processing device includes a board card and 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. According to the data processing method, the data processing 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.