Invention Grant
- Patent Title: Multiplication-free approximation for neural networks and sparse coding
-
Application No.: US17067979Application Date: 2020-10-12
-
Publication No.: US11232273B2Publication Date: 2022-01-25
- 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
- Main IPC: G06K7/10
- IPC: G06K7/10 ; G06N20/10 ; G06F7/487 ; G06F7/483 ; G06N3/04 ; G06F17/16 ; G06K7/14

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
- US20210027029A1 MULTIPLICATION-FREE APPROXIMATION FOR NEURAL NETWORKS AND SPARSE CODING Public/Granted day:2021-01-28
Information query