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.

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: 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 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.

Abstract: The present disclosure relates to the field of display technologies, and provides a display apparatus, including: a support and a display module, wherein the support includes a support part and a rotation part; a frame is disposed at a periphery of the display module, and includes two first frame segments which are oppositely arranged and two second frame segments which are oppositely arranged; one end of the rotation part is pivoted to the support part; a rotation axis of the rotation part is a center line of the rotation part; the other end of the rotation part is fixedly connected to any one of the first frame segments or the second frame segments; the axis of the rotation part is at a first included angle with the frame; and the first included angle is used for switching of the display module between a landscape-screen mode and a portrait-screen mode.

Abstract: The present disclosure relates to a converter for data type conversion, a method for data type conversion, an integrated circuit chip, and a calculation apparatus, where the calculation apparatus may be included in a combined processing apparatus, where the combined processing apparatus may further include a general interconnection interface and other processing apparatus. The calculation apparatus interacts with other processing apparatus to jointly complete a calculation operation specified by a user. The combined processing apparatus may further include a storage apparatus. The storage apparatus is connected to the calculation apparatus and other processing apparatus, respectively. The storage apparatus is used for storing data of the calculation apparatus and other processing apparatus. Solutions of the present disclosure may be widely applied to various data type conversion applications.

Abstract: An object recognition method, apparatus, device and storage medium are provided. The method includes the following steps: a real-time image where a screen includes an object operation region is acquired; image reference points that match with at least two reference origins in the real-time image is determined according to the mapping relationship between the real-time image and a preset standard image, the reference origin is a point within the object operation region in the preset standard image; and an object within the object operation region is recognized according to a pixel coordinate of the preset standard image and a reference pixel coordinate of the image reference point in the real-time image to obtain a target recognition result of the object.

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 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: Disclosed are various approaches to anticipating future resource consumption based on user sessions. A message comprising a prediction of a future number of concurrent user sessions to be hosted by a virtual machine within a predefined future interval of time is received. It is then determined whether the future number of concurrent user sessions will cause the virtual machine to cross a predefined resource threshold during the predefined future interval of time. Then, a message is sent to a first hypervisor hosting the virtual machine to migrate the virtual machine to a second hypervisor.

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: An integrated circuit chip apparatus and a processing method performed by an integrated circuit chip apparatus are disclosed. The disclosed integrated circuit chip apparatus and processing method are 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 reduced computational cost 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: 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 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: 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 image processing method includes obtaining a first quantity of to-be-analyzed images and performing fusion and enhancement processing on the first quantity of to-be-analyzed images through an image analysis model to obtain a first target image. Each to-be-analyzed image corresponds to a different target modality of a target imaging object. The first target image is used to enhance display of a distribution area of an analysis object of the first quantity of to-be-analyzed images. The analysis object belongs to the imaging object. The image analysis model is obtained by training a second quantity of sample images corresponding to different sample modalities. The first quantity is less than or equal to the second quantity. The target modality belongs to the sample modalities.