Invention Grant
- Patent Title: Optimized quantization for reduced resolution neural networks
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Application No.: US16739484Application Date: 2020-01-10
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Publication No.: US11601134B2Publication Date: 2023-03-07
- Inventor: Akshay Malhotra , Thomas Rocznik , Christian Peters
- Applicant: Robert Bosch GmbH
- Applicant Address: DE Stuttgart
- Assignee: Robert Bosch GmbH
- Current Assignee: Robert Bosch GmbH
- Current Assignee Address: DE Stuttgart
- Agency: Brooks Kushman P.C.
- Main IPC: G06N3/10
- IPC: G06N3/10 ; G06N3/08 ; H03M7/24 ; G06F17/18 ; G06N20/00 ; G06N5/046 ; G06F17/16 ; G06F17/15

Abstract:
A system and method for generating and using fixed-point operations for neural networks includes converting floating-point weighting factors into fixed-point weighting factors using a scaling factor. The scaling factor is defined to minimize a cost function and the scaling factor is derived from a set of multiples of a predetermined base. The set of possible scaling function is defined to reduce the computational effort for evaluating the cost function for each of a number of possible scaling factors. The system and method may be implemented in one or more controllers that are programmed to execute the logic.
Public/Granted literature
- US20210218414A1 OPTIMIZED QUANTIZATION FOR REDUCED RESOLUTION NEURAL NETWORKS Public/Granted day:2021-07-15
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