Invention Grant
- Patent Title: Concurrent optimization of machine learning model performance
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Application No.: US16515711Application Date: 2019-07-18
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Publication No.: US11907810B2Publication Date: 2024-02-20
- Inventor: Serag Gadelrab , James Esliger , Meghal Varia , Kyle Ernewein , Alwyn Dos Remedios , George Lee
- Applicant: QUALCOMM Incorporated
- Applicant Address: US CA San Diego
- Assignee: QUALCOMM Incorporated
- Current Assignee: QUALCOMM Incorporated
- Current Assignee Address: US CA San Diego
- Agency: QUALCOMM Incorporated
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F11/34 ; G06N5/04

Abstract:
Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.
Public/Granted literature
- US20210019652A1 CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE Public/Granted day:2021-01-21
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