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US Patent 11934940 AI processor simulation

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119349400
Patent Inventor Names
Yao Zhang0
Xiaofu Meng0
Shaoli Liu0
Date of Patent
March 19, 2024
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Patent Application Number
167206550
Date Filed
December 19, 2019
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Patent Citations
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US Patent 7242414 Processor having a compare extension of an instruction set architecture
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US Patent 7406451 Method for training neural networks
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US Patent 7721128 Implementation of thermal throttling logic
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US Patent 7945607 Data processing apparatus and method for converting a number between fixed-point and floating-point representations
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US Patent 8051117 Shift significand of decimal floating point data
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US Patent 8190664 Employing a mask field of an instruction to encode a sign of a result of the instruction
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US Patent 8560591 Detection of potential need to use a larger data format in performing floating point operations
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US Patent 8694572 Decimal floating-point fused multiply-add unit
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...
Patent Primary Examiner
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Jacob Petranek
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CPC Code
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G06F 9/3869
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G06F 9/30014
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Patent 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.

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