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Quantization training with two-level bit width
- Authors
- Kang, Hansung; Lee, Yongjoo; Cho, Dongbin; Lee, Jaeyoung; Kang, Mincheal; Kim, Younghoon; Seo, Jiwon
- Issue Date
- Feb-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Fake Single Precision Training; Qaunt Noise; Quantization Aware Training
- Citation
- 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022, pp 1 - 4
- Pages
- 4
- Indexed
- SCOPUS
- Journal Title
- 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
- Start Page
- 1
- End Page
- 4
- ISSN
- 0000-0000
- Abstract
- As the DNN model becomes more complex, the number of parameters constituting the model increases and requires a large amount of computation. Recently, a quantization technique that reduces the memory of the model and enables efficient computation has been studied. In this paper, we propose Fake Single Precision Training (FST) to increase accuracy by using a high bit range for weight and a low bit range for activation output with a certain probability. FST improved the accuracy of the model by applying the features of Google's Quantization Aware Training and FaceBook's Quant Noise method.
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