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US Patent 12079723 Optimizing neural network structures for embedded systems

Patent 12079723 was granted and assigned to Tesla, Inc. on September, 2024 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
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Tesla, Inc.
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Current Assignee
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Tesla, Inc.
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
120797231
Patent Inventor Names
Forrest Nelson Iandola1
Harsimran Singh Sidhu1
Daniel Paden Tomasello1
Paras Jagdish Jain1
Date of Patent
September 3, 2024
1
Patent Application Number
181835151
Date Filed
March 14, 2023
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Patent Citations
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US Patent 8165380 Method, apparatus and program for processing mammographic image
1
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US Patent 8190537 Feature selection for large scale models
1
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US Patent 8369633 Video codec method and system
1
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US Patent 8406515 Method for automatically cropping digital images
1
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US Patent 8509478 Detection of objects in digital images
1
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US Patent 8588470 Methods and systems for improved license plate signature matching by similarity learning on synthetic images
1
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US Patent 8620837 Determination of a basis for a new domain model based on a plurality of learned models
1
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US Patent 8744174 Image processing apparatus, image processing method, and storage medium
1
...
Patent Primary Examiner
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Douglas M Slachta
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CPC Code
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G05D 1/0214
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G05D 1/0221
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G05B 13/027
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G05D 1/0088
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G06N 3/08
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G06F 9/45533
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G06N 3/10
1
Patent abstract

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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