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.

Abstract: A wake up signal scheme between a network and a user equipment (UE) operating in a Discontinuous Reception (DRX) state. The UE monitors a wake up signal (WUS) occasion to determine whether a WUS is received during the WUS occasion, when the WUS is received, determines whether the WUS identifies the UE, when the WUS is received and identifies the UE, monitors an OnDuration of a Discontinuous Reception (DRX) cycle corresponding to the WUS and when the WUS is not received or the WUS does not identify the UE, remains in a sleep state during the OnDuration corresponding to the WUS.

Abstract: A wake up signal scheme between a network and a user equipment (UE) operating in a Discontinuous Reception (DRX) state. The UE monitors a wake up signal (WUS) occasion to determine whether a WUS is received during the WUS occasion, when the WUS is received, determines whether the WUS identifies the UE, when the WUS is received and identifies the UE, monitors an OnDuration of a Discontinuous Reception (DRX) cycle corresponding to the WUS and when the WUS is not received or the WUS does not identify the UE, remains in a sleep state during the OnDuration corresponding to the WUS.

Abstract: Provided are a power distribution unit and a rack system. The power distribution unit includes: a terminal block (1) having an input terminal (11) for power supply and a lead-out terminal (12) for power supply; at least one wiring pad (2) respectively electrically connected to the lead-out terminal (12); a plurality of power output terminal groups (3), each power output terminal group (3) being composed of at least one power output terminal (31), and each power output terminal (31) being electrically connected to a respective wiring pad (2); and a plurality of switch mechanisms (4) provided in such a way that a switch mechanism (4) is correspondingly provided for a power output terminal group (3), where the switch mechanism (4) causes the corresponding power output terminal group (3) to be electrically connected to or electrically disconnected from the corresponding wiring pad (2).

Abstract: Provided is a server (100), which is applicable to the technical field of electronic devices. The server (100) includes a server main body (110), a power supply (120), and a heat conduction layer (130), where the server main body (110) includes a chassis (111), a first circuit board assembly (112), and a heat dissipation fan assembly (113); the first circuit board assembly (112) is installed in the chassis (111); and the heat dissipation fan assembly (113) is installed on the chassis (111) to dissipate heat of the first circuit board assembly (112). The power supply (120) is installed on one side of the chassis (111); and the heat conduction layer (130) is arranged between the power supply (120) and the chassis (111) to conduct heat generated by the power supply (120) to the chassis (111).

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 apparatus and related products. The products include a control module including an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store computation instructions associated with an artificial neural network operation; the instruction processing unit is configured to parse the computation instructions to obtain a plurality of operation instructions; and the storage queue unit is configured to store an instruction queue, where the instruction queue includes a plurality of operation instructions or computation instructions to be executed in the sequence of the queue. By adopting the above-mentioned method, the present disclosure can improve the operation efficiency of related products when performing operations of a neural network model.

Abstract: The present disclosure provides a data processing apparatus and related products. The products include a control module including an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store computation instructions associated with an artificial neural network operation; the instruction processing unit is configured to parse the computation instructions to obtain a plurality of operation instructions; and the storage queue unit is configured to store an instruction queue, where the instruction queue includes a plurality of operation instructions or computation instructions to be executed in the sequence of the queue. By adopting the above-mentioned method, the present disclosure can improve the operation efficiency of related products when performing operations of a neural network model.

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: A negotiation device conducting automated negotiation by transmitting and receiving proposals to and from a counterpart device includes an estimating unit configured to estimate a negotiation characteristic of the counterpart device by using information corresponding to a proposal transmitted by the negotiation device.

Abstract: The present disclosure provides a data processing apparatus and related products. The products include a control module including an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store computation instructions associated with an artificial neural network operation; the instruction processing unit is configured to parse the computation instructions to obtain a plurality of operation instructions; and the storage queue unit is configured to store an instruction queue, where the instruction queue includes a plurality of operation instructions or computation instructions to be executed in the sequence of the queue. By adopting the above-mentioned method, the present disclosure can improve the operation efficiency of related products when performing operations of a neural network model.

Abstract: The present disclosure provides a data processing apparatus and related products. The products include a control module including an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store computation instructions associated with an artificial neural network operation; the instruction processing unit is configured to parse the computation instructions to obtain a plurality of operation instructions; and the storage queue unit is configured to store an instruction queue, where the instruction queue includes a plurality of operation instructions or computation instructions to be executed in the sequence of the queue. By adopting the above-mentioned method, the present disclosure can improve the operation efficiency of related products when performing operations of a neural network model.

Abstract: The present disclosure provides a data processing apparatus and related products. The products include a control module including an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store computation instructions associated with an artificial neural network operation; the instruction processing unit is configured to parse the computation instructions to obtain a plurality of operation instructions; and the storage queue unit is configured to store an instruction queue, where the instruction queue includes a plurality of operation instructions or computation instructions to be executed in the sequence of the queue. By adopting the above-mentioned method, the present disclosure can improve the operation efficiency of related products when performing operations of a neural network model.

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.