DATA PARALLELISM IN DISTRIBUTED TRAINING OF ARTIFICIAL INTELLIGENCE MODELS

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United States of America Patent

APP PUB NO 20220283820A1
SERIAL NO

17752044

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Abstract

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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 adjusted to reduce the communication overhead. Multi-level parallel parameters reduction may be performed at the parameter server and the target device.

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Patent Owner(s)

Patent OwnerAddress
MICROSOFT TECHNOLOGY LICENSING LLCONE MICROSOFT WAY REDMOND WA 98052

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Bharadwaj, Sujeeth Subramanya Milpitas, US 5 18
Mesmakhosroshahi, Maral Sunnyvale, US 15 81
Patel, Devangkumar Freemont, US 2 7
Pudipeddi, Bharadwaj San Jose, US 29 112
Tremblay, Marc Bellevue, US 273 6180
Xi, Jinwen Sunnyvale, US 22 66

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