Invention Grant
- Patent Title: Multiplication-free approximation for neural networks and sparse coding
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Application No.: US17554255Application Date: 2021-12-17
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Publication No.: US11714977B2Publication Date: 2023-08-01
- Inventor: Gautham Chinya , Shihao Ji , Arnab Paul
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Intel Corporation
- Current Assignee: Intel Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Jordan IP Law, LLC
- The original application number of the division: US17067979 2020.10.12
- Main IPC: G06K7/10
- IPC: G06K7/10 ; G06N20/10 ; G06F7/487 ; G06F7/483 ; G06N3/045 ; G06F17/16 ; G06K7/14 ; G06N3/04

Abstract:
Systems, apparatuses and methods may provide for replacing floating point matrix multiplication operations with an approximation algorithm or computation in applications that involve sparse codes and neural networks. The system may replace floating point matrix multiplication operations in sparse code applications and neural network applications with an approximation computation that applies an equivalent number of addition and/or subtraction operations.
Public/Granted literature
- US20220108093A1 MULTIPLICATION-FREE APPROXIMATION FOR NEURAL NETWORKS AND SPARSE CODING Public/Granted day:2022-04-07
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