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US Patent 11636333 Optimizing neural network structures for embedded systems

Patent 11636333 was granted and assigned to Tesla (company) on April, 2023 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Tesla (company)
Tesla (company)
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Current Assignee
Tesla (company)
Tesla (company)
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
116363330
Date of Patent
April 25, 2023
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Patent Application Number
165224110
Date Filed
July 25, 2019
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Patent Citations
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US Patent 10167800 Hardware node having a matrix vector unit with block-floating point processing
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US Patent 10169680 Object identification and labeling tool for training autonomous vehicle controllers
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US Patent 10192016 Neural network based physical synthesis for circuit designs
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US Patent 10216189 Systems and methods for prioritizing object prediction for autonomous vehicles
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US Patent 10228693 Generating simulated sensor data for training and validation of detection models
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US Patent 10242293 Method and program for computing bone age by deep neural network
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US Patent 10248121 Machine-learning based autonomous vehicle management system
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US Patent 10262218 Simultaneous object detection and rigid transform estimation using neural network
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...
Patent Primary Examiner
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Douglas M Slachta
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CPC Code
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G06F 9/45533
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G06N 3/08
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G06N 3/10
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A model training and implementation pipeline trains models for individual embedded systems. The pipeline iterates through multiple models and estimates the performance of the models. During a model generation stage, the pipeline translates the description of the model together with the model parameters into an intermediate representation in a language that is compatible with a virtual machine. The intermediate representation is agnostic or independent to the configuration of the target platform. During a model performance estimation stage, the pipeline evaluates the performance of the models without training the models. Based on the analysis of the performance of the untrained models, a subset of models is selected. The selected models are then trained and the performance of the trained models are analyzed. Based on the analysis of the performance of the trained models, a single model is selected for deployment to the target platform.

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