Invention Grant
- Patent Title: Methods and apparatus to optimize execution of a machine learning model
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Application No.: US16456863Application Date: 2019-06-28
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Publication No.: US11507838B2Publication Date: 2022-11-22
- Inventor: Mikael Bourges-Sevenier , Adam Herr , Sridhar Sharma , Derek Gerstmann , Todd Anderson , Justin Gottschlich
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Intel Corporation
- Current Assignee: Intel Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Hanley, Flight & Zimmerman, LLC
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F17/16

Abstract:
Methods, apparatus, systems and articles of manufacture to optimize execution of a machine learning model are disclosed. An example apparatus includes a quantizer to quantize a layer of a model based on an execution constraint, the layer of the model represented by a matrix. A packer is to pack the quantized layer of the matrix to create a packed layer represented by a packed matrix, the packed matrix having non-zero values of the matrix grouped together along at least one of a row or a column of the matrix. A blocker is to block the packed layer into a blocked layer by dividing the non-zero values in the packed matrix into blocks. A fuser is to fuse the blocked layer into a pipeline. A packager is to package the pipeline into a binary.
Public/Granted literature
- US20190325314A1 METHODS AND APPARATUS TO OPTIMIZE EXECUTION OF A MACHINE LEARNING MODEL Public/Granted day:2019-10-24
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