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公开(公告)号：EP4024281A1
公开(公告)日：2022-07-06
申请号：EP20856760.2
申请日：2020-03-31
发明人： ZHANG, Yao , JIANG, Guang , ZHANG, Xishan , ZHOU, Shiyi , HUANG, Di , LIU, Chang , GUO, Jiaming
摘要： Embodiments of the present disclosure relate to a method and an apparatus for processing data, and related products. The embodiments of the present disclosure relate to a board card, which includes 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 realize data transmission 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 may be used to perform artificial intelligence computations.
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公开(公告)号：EP4009184A1
公开(公告)日：2022-06-08
申请号：EP21217811.5
申请日：2019-10-18
发明人： ZHANG, Yao , LIU, Shaoli , LIANG, Jun , CHEN, Yu , LI, Zhen
IPC分类号： G06F15/173 , G06F15/78 , G06N3/063 , G06N3/08 , G06F15/163
摘要： 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.
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公开(公告)号：EP3651078A1
公开(公告)日：2020-05-13
申请号：EP19213389.0
申请日：2018-09-03
发明人： ZHANG, Yao , WANG, Bingrui
摘要： 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 transmit 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 processing efficiency of training operations.
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公开(公告)号：EP3651076A1
公开(公告)日：2020-05-13
申请号：EP19212755.3
申请日：2018-09-03
发明人： ZHANG, Yao , WANG, Bingrui
摘要： 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 transmit 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 processing efficiency of training operations.
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公开(公告)号：EP3651073A1
公开(公告)日：2020-05-13
申请号：EP19212752.0
申请日：2018-09-03
发明人： ZHANG, Yao , WANG, Bingrui
摘要： 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 transmit 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 processing efficiency of training operations.
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公开(公告)号：EP4024287A1
公开(公告)日：2022-07-06
申请号：EP20858492.0
申请日：2020-08-26
发明人： ZHANG, Yao , JIANG, Guang , ZHANG, Xishan , ZHOU, Shiyi , HUANG, Di , LIU, Chang , GUO, Jiaming
IPC分类号： G06N3/063
摘要： Embodiments of the present disclosure relate to a method and an apparatus for processing data, and related products. The embodiments of the present disclosure relate to a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip, where 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 realize data transmission between the artificial intelligence chip and the external device. The control component is used to monitor a state of the artificial intelligence chip. The board card may be used to perform artificial intelligence computations.
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公开(公告)号：EP4024282A1
公开(公告)日：2022-07-06
申请号：EP20857908.6
申请日：2020-08-26
发明人： ZHANG, Yao , JIANG, Guang , ZHANG, Xishan , ZHOU, Shiyi , HUANG, Di , LIU, Chang , GUO, Jiaming
摘要： The present disclosure relates to a method, a device, and related products for processing data. In an embodiment of the present disclosure, when processing data related to a neural network, an optimal truncation threshold value for a plurality of pieces of data is determined. The data is truncated through the truncation data threshold, and the plurality of pieces of data is quantized from a high-precision format to a low-precision format. The method in the present disclosure can ensure the precision of data processing as high as possible while reducing the amount of data processing. In addition, the method also helps to significantly reduce the amount of data transmission, thereby greatly accelerating the data exchange among a plurality of computing devices.
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8.发明公开
公开(公告)号：EP3798850A1
公开(公告)日：2021-03-31
申请号：EP19824842.9
申请日：2019-06-25
发明人： SU, Zhenyu , ZHANG, Dingfei , ZHOU, Xiaoyong , ZHANG, Yao , LI, Chongwen , DU, Zidong , LIU, Shaoli
摘要： The present invention relates to an on-chip code breakpoint debugging method, an on-chip processor, and a chip breakpoint debugging system. The method comprises: the on-chip processor starts and executes an on-chip code, and an output function is set at a breakpoint position of the on-chip code; the on-chip processor obtains output information of the output function, the output information is output information of the output function when the on-chip code is executed to the output function; the on-chip processor stores the output information into an off-chip memory. In the embodiments of the present invention, according to the output information, which is stored in the off-chip memory, of the output function, the on-chip processor can obtain execution conditions of breakpoints of the on-chip code in real time, can achieve the purpose of debugging multiple breakpoints in the on-chip code at the same time, and debugging efficiency of the on-chip code is improved.
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公开(公告)号：EP3651077A1
公开(公告)日：2020-05-13
申请号：EP19212756.1
申请日：2018-09-03
发明人： ZHANG, Yao , WANG, Bingrui
摘要： 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 transmit 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 processing efficiency of training operations.
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公开(公告)号：EP3651074A1
公开(公告)日：2020-05-13
申请号：EP19212753.8
申请日：2018-09-03
发明人： ZHANG, Yao , WANG, Bingrui
摘要： 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 transmit 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 processing efficiency of training operations.
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