Abstract: The present disclosure relates to a method, a device and a computation apparatus for allocating a space address to data in a memory, where the computation apparatus is included in a combined processing apparatus, which includes a general interconnection interface and other processing apparatuses. The computation apparatus interacts with other processing apparatuses to jointly complete computations specified by the user. The combined processing apparatus also includes a storage apparatus. The storage apparatus is respectively connected to the computation apparatus and the other processing apparatuses, and is used for storing data of the computation apparatus and other processing apparatuses. The technical solutions of the present disclosure improve utilization of storage space of the memory.

Abstract: The technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.

Abstract: The present disclosure provides a data processing method, a board card device, a computer equipment, and a storage medium for data quantization. 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, where the artificial intelligence chip is connected to the storage device, the control device, and the interface apparatus, respectively. The storage component is configured to store data; the interface device is configured to implement data transmission between the artificial intelligence chip and an external equipment; and the control component is configured to monitor a state of the artificial intelligence chip.

Abstract: The present disclosure relates to a device and a method for implementing live migration, where an on-chip system of the present disclosure is comprised in an integrated circuit apparatus, which comprises a general interconnection interface and other processing apparatuses. The computation apparatus interacts with other processing apparatuses to jointly complete computing operations specified by users. The integrated circuit apparatus also comprises a storage apparatus. The storage apparatus is respectively connected to the computation apparatus and other processing apparatuses and is used for storing data of the computation apparatus and other processing apparatuses.

Abstract: A system for fusing operators of a neural network is included in a combined processing apparatus. The combined processing apparatus includes an interface apparatus and other processing apparatus. A computing processing apparatus interacts with other processing apparatus to jointly complete a computing operation specified by a user. The combined processing apparatus further includes a storage apparatus. The storage apparatus is connected to the apparatus and other processing apparatus, respectively. The storage apparatus is configured to store data of the apparatus and other processing apparatus. A solution of the present disclosure improves efficiency of various operations in data processing fields including, for example, an artificial intelligence field, thus reducing overall overheads and costs of the 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: 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 technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.

Abstract: The present disclosure relates to a device and a method for implementing live migration, where an on-chip system of the present disclosure is comprised in an integrated circuit apparatus, which comprises a general interconnection interface and other processing apparatuses. The computation apparatus interacts with other processing apparatuses to jointly complete computing operations specified by users. The integrated circuit apparatus also comprises a storage apparatus. The storage apparatus is respectively connected to the computation apparatus and other processing apparatuses and is used for storing data of the computation apparatus and other processing apparatuses.

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 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 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 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 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 relates to a device and a method for implementing live migration, where an on-chip system of the present disclosure is comprised in an integrated circuit apparatus, which comprises a general interconnection interface and other processing apparatuses. The computation apparatus interacts with other processing apparatuses to jointly complete computing operations specified by users. The integrated circuit apparatus also comprises a storage apparatus. The storage apparatus is respectively connected to the computation apparatus and other processing apparatuses and is used for storing data of the computation apparatus and other processing apparatuses.

Abstract: The present disclosure relates to a method, a device and a computation apparatus for allocating a space address to data in a memory, where the computation apparatus is included in a combined processing apparatus, which includes a general interconnection interface and other processing apparatuses. The computation apparatus interacts with other processing apparatuses to jointly complete computations specified by the user. The combined processing apparatus also includes a storage apparatus. The storage apparatus is respectively connected to the computation apparatus and the other processing apparatuses, and is used for storing data of the computation apparatus and other processing apparatuses. The technical solutions of the present disclosure improve utilization of storage space of the memory.

Abstract: A computation apparatus according to an embodiment is included in an integrated circuit apparatus, which includes a general interconnection interface and other processing apparatuses. The computation apparatus interacts with other processing apparatuses to jointly complete computation operations specified by users. The integrated circuit apparatus also includes a storage apparatus. The storage apparatus is respectively connected to the computation apparatus and the other processing apparatuses and is used for storing data of the computation apparatus and other processing apparatuses.

Abstract: The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.

Abstract: The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.