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US Patent 11520592 Executing large artificial intelligence models on memory-constrained devices

Patent 11520592 was granted and assigned to Microsoft on December, 2022 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
Microsoft
Microsoft
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Current Assignee
Microsoft
Microsoft
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
115205920
Patent Inventor Names
Tiyasa Mitra0
Bharadwaj Pudipeddi0
Gautham Popuri0
Layali Rashid0
Maral Mesmakhosroshahi0
Marc Tremblay0
Mohit Mittal0
Date of Patent
December 6, 2022
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Patent Application Number
165777790
Date Filed
September 20, 2019
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Patent Citations
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US Patent 10698900 Generating a distributed execution model with untrusted commands
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US Patent 10726009 Query processing using query-resource usage and node utilization data
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US Patent 10795884 Dynamic resource allocation for common storage query
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US Patent 10832120 Systems and methods for a multi-core optimized recurrent neural network
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US Patent 10878567 System to collect and identify skin conditions from images and expert knowledge
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US Patent 11188327 Memory lookup computing mechanisms
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US Patent 11062214 Computerized system and method of open account processing
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US Patent 10019668 Scheduling neural network processing
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Patent Primary Examiner
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Alicia Baturay
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CPC Code
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H04L 67/34
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H04L 67/289
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G06F 9/3877
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G06N 3/08
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Methods, systems, apparatuses, and computer program products are described herein that enable execution of a large AI model on a memory-constrained target device that is communicatively connected to a parameter server, which stores a master copy of the AI model. The AI model may be dissected into smaller portions (e.g., layers or sub-layers), and each portion may be executed as efficiently as possible on the target device. After execution of one portion of the AI model is finished, another portion of the AI model may be downloaded and executed at the target device. To improve efficiency, the input samples may be divided into microbatches, and a plurality of microbatches executing in sequential order may form a minibatch. The size of the group of microbatches or minibatch can be manually or automatically adjusted to reduce the communication overhead.

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