Invention Publication
- Patent Title: QUANTIZATION METHOD FOR NEURAL NETWORK MODEL AND DEEP LEARNING ACCELERATOR
-
Application No.: US17560010Application Date: 2021-12-22
-
Publication No.: US20230196094A1Publication Date: 2023-06-22
- Inventor: Chih-Cheng LU , Jin-Yu LIN , Kai-Cheung JUANG
- Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE
- Applicant Address: TW Hsinchu
- Assignee: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE
- Current Assignee: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE
- Current Assignee Address: TW Hsinchu
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04

Abstract:
A quantization method for neural network model includes following steps: initializing a weight array of a neural network model, wherein the weight array includes a plurality of initial weights; performing a quantization procedure to generate a quantized weight array according to the weight array, wherein the quantized weight array includes a plurality of quantized weights within a fixed range; performing a training procedure of the neural network model according to the quantized weight array; and determining whether a loss function is convergent in the training procedure and outputting a post-trained quantized weight array when the loss function is convergent.
Information query