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: The present application provides a displaying device and a controlling method thereof, which relates to the technical field of displaying. The displaying device can solve the problem of residual images generated in power-off and greatly improve the product quality and the user experience.

Abstract: The present application relates to a network-on-chip data processing method. The method is applied to a network-on-chip processing system, the network-on-chip processing system is used for executing machine learning calculation, and the network-on-chip processing system comprises a storage device and a calculation device. The method comprises: accessing the storage device in the network-on-chip processing system by means of a first calculation device in the network-on-chip processing system and obtaining first operation data; performing an operation on the first operation data by means of the first calculation device to obtain a first operation result; and sending the first operation result to a second calculation device in the network-on-chip processing system. According to the method, operation overhead can be reduced and data read/write efficiency can be improved.

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: Provided is a red mud-based composite calcium ferrite and a preparation method and use thereof. The preparation method of the red mud-based composite calcium ferrite includes the following steps: mixing red mud and a calcium source, and roasting an obtained mixture in an oxygen-containing atmosphere to obtain the red mud-based composite calcium ferrite; where the calcium source is selected from the group consisting of lime and calcium carbonate. In the present disclosure, the composite calcium ferrite is prepared using a solid waste red mud, with a greatly reduced cost of raw materials; on the other hand, compared with traditional calcium ferrite, the composite calcium ferrite mainly has phase structures of CaFe2O4, Ca2FeAlO5, and Ca2Fe2O5. Therefore, the composite calcium ferrite has a lower melting point, a higher lime dissolution efficiency, and better fluxing and dephosphorization effects during primary smelting and refining of molten steel, and has broad prospects for use in industry.

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 method and device for identifying a state, electronic device and computer-readable storage medium are provided. The method includes: acquiring a to-be-detected image for a specific scene and determining a region of interest in the to-be-detected image; the region of interest being a region obtained by subtracting an occlusion range of the storage container in a closed state from an occlusion range in an open state; determining a positional relation between the region of interest and specific object regions where the specific objects are positioned in the to-be-detected image; determining at least one value based on values of pixels in the region of interest, when the positional relation represents that there is no specific object region partially overlapping the region of interest; and determining whether the storage container is in an open state or closed state based on the at least one value and a preset value range.

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: Disclosed are a FAAS distributed computing method and apparatus. The method includes: decomposing a computation task into multiple steps with correlation and execution order, and constructing multiple mirror images and multiple method groups respectively; creating multiple dockers in a process and allocating hardware resources according to the mirror image corresponding to a specific step; transferring, according to a dynamic pointer of the corresponding method group, a data processing result to the method group corresponding to the next step, and simultaneously storing a generated intermediate result in a distributed memory file system; modifying dynamic pointers of the multiple method groups in real-time to dynamically adjust the correlation and execution order of the multiple steps; monitoring a running state of each method in the multiple method groups, restarting the docker of the method or loading latest docker snapshot of the method and expanding or reducing a capacity of the method groups.

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: Embodiments of the present disclosure disclose a data processing method based on an intelligent contract, a device, and a storage medium. The method includes: receiving a firmware update request corresponding to a first server node transmitted by an execution node, the firmware update request including an updated version parameter of the first server node; invoking an intelligent contract according to the firmware update request; obtaining, from a blockchain based on the intelligent contract, a firmware version update record and a firmware version release record that are associated with the first server node; and determining validity of the firmware update request according to the firmware version update record, the firmware version release record, and the updated version parameter. The firmware version release record is determined by a release node on the blockchain based on a consensus mechanism.

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.

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.