Quantization Method Based on Weight Compression and Format Simplification
Author:
Affiliation:
1. National Chung Cheng University,Advanced Institute of Manufacturing with High-Tech Innovations,Department of Computer Science and Information Engineering,ChiaYi,Taiwan
Funder
National Science Council
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10042470/10042811/10042824.pdf?arnumber=10042824
Reference9 articles.
1. Uniformly most powerful Bayesian tests
2. Back-of-the-Envelope Problems;moore;Univ California Berkeley,1987
3. Efficient Dynamic Fixed-Point Quantization of CNN Inference Accelerators for Edge Devices
4. Compiling ONNX neural network models using MLIR;jin;arXiv preprint arXiv 2008 00863,2020
5. Utilizing explainable AI for quantization and pruning of deep neural networks;sabih;arXiv preprint arXiv 2008 09234,2020
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