Hardware and Parameter-Aware ML Model GPU Efficiency Tuning Systems
Summary
USPTO published patent application US20260099757A1 for hardware and parameter-aware machine learning model GPU efficiency tuning systems. The application includes claims for methods and systems that receive ML training requests with fixed and dynamic configurations, generate task embeddings, train prediction modules on known configurations, and return optimal training efficiency configurations based on model utilization scores. Inventors include Pin-Lun Hsu, Vignesh KOTHAPALLI, Animesh SINGH, Qingquan SONG, Yun DAI, and Shao TANG. Filing date was October 4, 2024, with application number 18906517.
What changed
USPTO published a patent application for machine learning GPU efficiency tuning systems. The disclosed invention relates to methods for optimizing graphics processing unit utilization during ML model training by receiving fixed and dynamic configurations, generating task embeddings, and training prediction modules to output optimal training efficiency configurations.
For applicants and technology companies developing ML training infrastructure, this patent application represents potential prior art for GPU optimization technologies. The CPC classification G06N 20/00 indicates machine learning applications. No compliance obligations or deadlines are associated with this publication.
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Apr 14, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
HARDWARE AND PARAMETER-AWARE MACHINE LEARNING MODEL GPU EFFICIENCY TUNING SYSTEMS
Application US20260099757A1 Kind: A1 Apr 09, 2026
Inventors
Pin-Lun HSU, Vignesh KOTHAPALLI, Animesh SINGH, Qingquan SONG, Yun DAI, Shao TANG
Abstract
Aspects of the disclosure include methods and systems for machine learning, and specifically to hardware and parameter-aware machine learning (ML) model graphics processing unit (GPU) efficiency tuning systems. A method includes receiving a request corresponding to a machine learning model training task, a plurality of fixed configurations, and a plurality of dynamic configurations. A task embedding is generated from the plurality of fixed configurations. A prediction module is trained on known dynamic and fixed configurations and, for each combination of a dynamic configuration and a fixed configuration, a respective model utilization score. A plurality of model utilization scores are generated for a plurality of respective candidate configurations sampled from the dynamic configurations. Responsive to receiving the request, a response is returned including an optimal training efficiency configuration for the training task according to the plurality of model utilization scores.
CPC Classifications
G06N 20/00
Filing Date
2024-10-04
Application No.
18906517
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