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US Patent 11562231 Neural networks for embedded devices

Patent 11562231 was granted and assigned to Tesla (company) on January, 2023 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Tesla (company)
Tesla (company)
Current Assignee
Tesla (company)
Tesla (company)
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11562231
Date of Patent
January 24, 2023
Patent Application Number
16559483
Date Filed
September 3, 2019
Patent Citations
‌
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
...
Patent Citations Received
‌
US Patent 11983630 Neural networks for embedded devices
0
Patent Primary Examiner
‌
Steven G Snyder

A neural network architecture is used that reduces the processing load of implementing the neural network. This network architecture may thus be used for reduced-bit processing devices. The architecture may limit the number of bits used for processing and reduce processing to prevent data overflow at individual calculations of the neural network. To implement this architecture, the number of bits used to represent inputs at levels of the network and the related filter masks may also be modified to ensure the number of bits of the output does not overflow the resulting capacity of the reduced-bit processor. To additionally reduce the load for such a network, the network may implement a “starconv” structure that permits the incorporation of nearby nodes in a layer to balance processing requirements and permit the network to learn from context of other nodes.

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