Invention Grant
- Patent Title: Generation of executable files corresponding to neural network models
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Application No.: US16260331Application Date: 2019-01-29
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Publication No.: US11645358B2Publication Date: 2023-05-09
- Inventor: Soumitra Chatterjee , Sunil Vishwanathpur Lakshminarasimha , Mohan Parthasarathy
- Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
- Applicant Address: US TX Houston
- Assignee: Hewlett Packard Enterprise Development LP
- Current Assignee: Hewlett Packard Enterprise Development LP
- Current Assignee Address: US TX Spring
- Agency: Trop, Pruner & Hu, P.C.
- Main IPC: G06F17/16
- IPC: G06F17/16 ; G06F9/44 ; G06F8/20 ; G06N3/04 ; G06N3/063 ; G06N3/02

Abstract:
In an example, a neural network program corresponding to a neural network model is received. The neural network program includes matrices, vectors, and matrix-vector multiplication (MVM) operations. A computation graph corresponding to the neural network model is generated. The computation graph includes a plurality of nodes, each node representing a MVM operation, a matrix, or a vector. Further, a class model corresponding to the neural network model is populated with a data structure pointing to the computation graph. The computation graph is traversed based on the class model. Based on the traversal, the plurality of MVM operations are assigned to MVM units of a neural network accelerator. Each MVM unit can perform a MVM operation. Based on assignment of the plurality of MVM operations, an executable file is generated for execution by the neural network accelerator.
Public/Granted literature
- US20200242189A1 GENERATION OF EXECUTABLE FILES CORRESPONDING TO NEURAL NETWORK MODELS Public/Granted day:2020-07-30
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