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: Provided are an integrated circuit chip apparatus and a related product, the integrated circuit chip apparatus being used for executing a multiplication operation, a convolution operation or a training operation of a neural network. The present technical solution has the advantages of a small amount of calculation and low power consumption.

Abstract: Provided are an integrated circuit chip apparatus and a related product, the integrated circuit chip apparatus being used for executing a multiplication operation, a convolution operation or a training operation of a neural network. The present technical solution has the advantages of a small amount of calculation and low power consumption.

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: A data processing method includes obtaining content of a descriptor when an operand of a first processing instruction includes the descriptor, where the descriptor is configured to indicate a shape of tensor data and to indicate data address of the tensor data, and executing the first processing instruction according to the content of the descriptor by determining the data address of the tensor data corresponding to the operand of the first processing instruction in a data storage space, according to the content of the descriptor, and according to the data address, executing data processing corresponding to the first processing instruction.

Abstract: The present disclosure provides a data processing method and an apparatus and a related product. 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 and an apparatus and a related product. 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: There is provides an operation module, which includes a memory, a register unit, a dependency relationship processing unit, an operation unit, and a control unit. The memory is configured to store a vector, the register unit is configured to store an extension instruction, and the control unit is configured to acquire and parse the extension instruction, so as to obtain a first operation instruction and a second operation instruction. An execution sequence of the first operation instruction and the second operation instruction can be determined, and an input vector of the first operation instruction can be read from the memory. The operation unit is configured to convert an expression mode of the input data index of the first operation instruction and to screen data, and to execute the first and second operation instruction according to the execution sequence, so as to obtain an extension instruction.

Abstract: The present disclosure discloses a neural network processing module, in which a mapping unit is configured to receive an input neuron and a weight, and then process the input neuron and/or the weight to obtain a processed input neuron and a processed weight; and an operation unit is configured to perform an artificial neural network operation on the processed input neuron and the processed weight. Examples of the present disclosure may reduce additional overhead of the device, reduce the amount of access, and improve efficiency of the neural network operation.

Abstract: A convolution operation method and a processing device for performing the same are provided. The method is performed by a processing device. The processing device includes a main processing circuit and a plurality of basic processing circuits. The basic processing circuits are configured to perform convolution operation in parallel. The technical solutions disclosed by the present disclosure can provide short operation time and low energy consumption.

Abstract: An integrated circuit chip device and related products are provided. The integrated circuit chip device is used for performing a multiplication operation, a convolution operation or a training operation of a neural network. The device has the advantages of small calculation amount and low power consumption.

Abstract: An integrated circuit chip device and related products are provided. The integrated circuit chip device is used for performing a multiplication operation, a convolution operation or a training operation of a neural network. The device has the advantages of small calculation amount and low power consumption.

Abstract: An integrated circuit chip device and related products are provided. The integrated circuit chip device is used for performing a multiplication operation, a convolution operation or a training operation of a neural network. The device has the advantages of small calculation amount and low power consumption.

Abstract: An integrated circuit chip device and related products are provided. The integrated circuit chip device is used for performing a multiplication operation, a convolution operation or a training operation of a neural network. The device has the advantages of small calculation amount and low power consumption.

Abstract: An integrated circuit chip device and related products are provided. The integrated circuit chip device is used for performing a multiplication operation, a convolution operation or a training operation of a neural network. The device has the advantages of small calculation amount and low power consumption.

Abstract: The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

Abstract: An integrated circuit chip device and related products are provided. The integrated circuit chip device is used for performing a multiplication operation, a convolution operation or a training operation of a neural network. The device has the advantages of small calculation amount and low power consumption.

Abstract: The present disclosure provides an integrated circuit chip device and a related product. The integrated circuit chip device includes: a primary processing circuit and a plurality of basic processing circuits. The primary processing circuit or at least one of the plurality of basic processing circuits includes the compression mapping circuits configured to perform compression on each data of a neural network operation. The technical solution provided by the present disclosure has the advantages of a small amount of computations and low power consumption.

Abstract: The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

Abstract: The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.