Invention Grant
- Patent Title: Energy efficient machine learning models
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Application No.: US16694442Application Date: 2019-11-25
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Publication No.: US11620499B2Publication Date: 2023-04-04
- Inventor: Jamie Menjay Lin , Daniel Hendricus Franciscus Fontijne , Edwin Chongwoo Park
- Applicant: QUALCOMM Incorporated
- Applicant Address: US CA San Diego
- Assignee: QUALCOMM Incorporated
- Current Assignee: QUALCOMM Incorporated
- Current Assignee Address: US CA San Diego
- Agency: Patterson & Sheridan, L.L.P
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04

Abstract:
Aspects described herein provide a method including: receiving input data at a machine learning model, comprising: a plurality of processing layers; a plurality of gate logics; a plurality of gates; and a fully connected layer; determining based on a plurality of gate parameters associated with the plurality of gate logics, a subset of the plurality of processing layers with which to process the input data; processing the input data with the subset of the plurality of processing layers and the fully connected layer to generate an inference; determining a prediction loss based on the inference and a training label associated with the input data; determining an energy loss based on the subset of the plurality of processing layers used to process the input data; and optimizing the machine learning model based on: the prediction loss; the energy loss; and a prior probability associated with the training label.
Public/Granted literature
- US20210158145A1 ENERGY EFFICIENT MACHINE LEARNING MODELS Public/Granted day:2021-05-27
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