Invention Grant
- Patent Title: Dynamic quantization for deep neural network inference system and method
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Application No.: US17128365Application Date: 2020-12-21
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Publication No.: US11580719B2Publication Date: 2023-02-14
- Inventor: Kumar Desappan , Manu Mathew , Pramod Kumar Swami , Praveen Eppa
- Applicant: Texas Instruments Incorporated
- Applicant Address: US TX Dallas
- Assignee: Texas Instruments Incorporated
- Current Assignee: Texas Instruments Incorporated
- Current Assignee Address: US TX Dallas
- Agent Michael T. Gabrik; Frank D. Cimino
- Priority: IN201741023782 20170706
- Main IPC: G06V10/28
- IPC: G06V10/28 ; G06K9/62 ; G06N3/04 ; G06N3/08 ; G06N3/063 ; G06V10/44

Abstract:
A method for dynamically quantizing feature maps of a received image. The method includes convolving an image based on a predicted maximum value, a predicted minimum value, trained kernel weights and the image data. The input data is quantized based on the predicted minimum value and predicted maximum value. The output of the convolution is computed into an accumulator and re-quantized. The re-quantized value is output to an external memory. The predicted min value and the predicted max value are computed based on the previous max values and min values with a weighted average or a pre-determined formula. Initial min value and max value are computed based on known quantization methods and utilized for initializing the predicted min value and predicted max value in the quantization process.
Public/Granted literature
- US20210150248A1 DYNAMIC QUANTIZATION FOR DEEP NEURAL NETWORK INFERENCE SYSTEM AND METHOD Public/Granted day:2021-05-20
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