Invention Grant
- Patent Title: Offline combination of convolutional/deconvolutional and batch-norm layers of convolutional neural network models for autonomous driving vehicles
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Application No.: US15451345Application Date: 2017-03-06
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Publication No.: US11308391B2Publication Date: 2022-04-19
- Inventor: Zhenhua Yu , Xiao Bo , Jun Zhou , Weide Zhang , Tony Han
- Applicant: Baidu USA LLC
- Applicant Address: US CA Sunnyvale
- Assignee: Baidu USA LLC
- Current Assignee: Baidu USA LLC
- Current Assignee Address: US CA Sunnyvale
- Agency: Womble Bond Dickinson (US) LLP
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G06F17/16 ; G06K9/00 ; G06K9/62

Abstract:
In one embodiment, a system to accelerate batch-normalized convolutional neural network (CNN) models is disclosed. The system extracts a plurality of first groups of layers from a first CNN model, each group of the first groups having a first convolutional layer and a first batch-norm layer. For each group of the plurality of first groups, the system calculates a first scale vector and a first shift vector based on the first batch-norm layer, and generates a second convolutional layer representing the corresponding group of the plurality of first groups based on the first convolutional layer and the first scale and the first shift vectors. The system generates an accelerated CNN model based on the second convolutional layer corresponding to the plurality of the first groups, such that the accelerated CNN model is utilized subsequently to classify an object perceived by an autonomous driving vehicle (ADV).
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