Abstract: The present disclosure relates to a video retrieval method, a method, system and device for generating a video retrieval mapping relationship, and a storage medium. The video retrieval method comprises: acquiring a retrieval instruction, wherein the retrieval instruction carries retrieval information for retrieving a target frame picture; and obtaining the target frame picture according to the retrieval information and a preset mapping relationship.

Abstract: The present disclosure provides a data pre-processing method and device and related computer device and storage medium. By storing the target output data corresponding to the target operation into the first memory close to the processor and reducing the time of reading the target output data, the occupation time of I/O read operations during the operation process can be reduced, and the speed and efficiency of the processor can be improved.

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 discloses a data processing method and related products, in which the data processing method includes: generating, by a general-purpose processor, a binary instruction according to device information of an AI processor, and generating an AI learning task according to the binary instruction; transmitting, by the general-purpose processor, the AI learning task to the cloud AI processor for running; receiving, by the general-purpose processor, a running result corresponding to the AI learning task; and determining, by the general-purpose processor, an offline running file according to the running result, where the offline running file is generated according to the device information of the AI processor and the binary instruction when the running result satisfies a preset requirement. By implementing the present disclosure, the debugging between the AI algorithm model and the AI processor can be achieved in advance.

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 invention relates to an operation method and device and a related product, the product comprises a control module, and the control module comprises 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 the artificial neural network operation; the instruction processing unit is used for analyzing the calculation instruction to obtain a plurality of operation instructions; the storage queue unit is used for storing instruction queues, and the instruction queues comprise a plurality of operation instructions or calculation instructions to be executed according to the sequence of the queues. Through the method, the operation efficiency of related products during operation of the neural network model can be improved.

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 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: A model conversion method is disclosed. The model conversion method includes obtaining model attribute information of an initial offline model and hardware attribute information of a computer equipment, determining whether the model attribute information of the initial offline model matches the hardware attribute information of the computer equipment according to the initial offline model and the hardware attribute information of the computer equipment and in the case when the model attribute information of the initial offline model does not match the hardware attribute information of the computer equipment, converting the initial offline model to a target offline model that matches the hardware attribute information of the computer equipment according to the hardware attribute information of the computer equipment and a preset model conversion rule.

Abstract: The present disclosure discloses a data processing method and related products, in which the data processing method includes: generating, by a general-purpose processor, a binary instruction according to device information of an AI processor, and generating an AI learning task according to the binary instruction; transmitting, by the general-purpose processor, the AI learning task to the cloud AI processor for running; receiving, by the general-purpose processor, a running result corresponding to the AI learning task; and determining, by the general-purpose processor, an offline running file according to the running result, where the offline running file is generated according to the device information of the AI processor and the binary instruction when the running result satisfies a preset requirement. By implementing the present disclosure, the debugging between the AI algorithm model and the AI processor can be achieved in advance.

Abstract: A fractal computing device according to an embodiment of the present application may be included in an integrated circuit device. The integrated circuit device includes a universal interconnect interface and other processing devices. The calculating device interacts with other processing devices to jointly complete a user specified calculation operation. The integrated circuit device may also include a storage device. The storage device is respectively connected with the calculating device and other processing devices and is used for data storage of the computing device and other processing devices.

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 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: Aspects for neural network operations with floating-point number of short bit length are described herein. The aspects may include a neural network processor configured to process one or more floating-point numbers to generate one or more process results. Further, the aspects may include a floating-point number converter configured to convert the one or more process results in accordance with at least one format of shortened floating-point numbers. The floating-point number converter may include a pruning processor configured to adjust a length of a mantissa field of the process results and an exponent modifier configured to adjust a length of an exponent field of the process results in accordance with the at least one format.

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: The present disclosure provides a neural network computation device and a neural network computation method. The device is configured to perform artificial neural network training operations including neural network multi-layer training operations. The present disclosure has technical effects of low cost and low energy consumption.

Abstract: The present disclosure discloses a data processing method and related products, in which the data processing method includes: generating, by a general-purpose processor, a binary instruction according to device information of an AI processor, and generating an AI learning task according to the binary instruction; transmitting, by the general-purpose processor, the AI learning task to the cloud AI processor for running; receiving, by the general-purpose processor, a running result corresponding to the AI learning task; and determining, by the general-purpose processor, an offline running file according to the running result, where the offline running file is generated according to the device information of the AI processor and the binary instruction when the running result satisfies a preset requirement. By implementing the present disclosure, the debugging between the AI algorithm model and the AI processor can be achieved in advance.

Abstract: The present disclosure provides a multi-purpose heat sink, a method of manufacturing the same, a board card, and a multi-purpose heat sink platform, where the multi-purpose heat sink is composed of a bracket (310) and heat dissipation components. The cost of the multi-purpose heat sink in the present disclosure is low.

Abstract: Disclosed is a heat dissipation device including a base, a plurality of heat pipes and a heat dissipation structure. The base includes a support plate, a plurality of through-pipe grooves are arranged in the support plate. Each heat pipe includes a heat absorption section and a heat transfer section connected to the heat absorption section, where the heat absorption section is arranged in the through-pipe grooves and at least one heat transfer section of the heat pipe is away from the support plate in the perpendicular direction to the support plate. The heat dissipation structure is arranged on the first surface of the support plate, and the heat transfer section of the plurality of heat pipes extends into the interior of the heat dissipation structure and thermally contacts the heat dissipation structure. The heat dissipation device of the present disclosure dissipates heat faster.