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Quantization noise in low bit quantization and iterative adaptation to quantization noise in quantizable neural networks. / Chudakov, D.; Goncharenko, A.; Alyamkin, S. и др.

в: Journal of Physics: Conference Series, Том 2134, № 1, 012004, 20.12.2021.

Результаты исследований: Научные публикации в периодических изданияхстатья по материалам конференцииРецензирование

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Chudakov D, Goncharenko A, Alyamkin S, Densidov A. Quantization noise in low bit quantization and iterative adaptation to quantization noise in quantizable neural networks. Journal of Physics: Conference Series. 2021 дек. 20;2134(1):012004. doi: 10.1088/1742-6596/2134/1/012004

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BibTeX

@article{9a1018eacb73487d8829c59e72f04459,
title = "Quantization noise in low bit quantization and iterative adaptation to quantization noise in quantizable neural networks",
abstract = "Quantization is one of the most popular and widely used methods of speeding up a neural network. At the moment, the standard is 8-bit uniform quantization. Nevertheless, the use of uniform low-bit quantization (4- and 6-bit quantization) has significant advantages in speed and resource requirements for inference. We present our quantization algorithm that offers advantages when using uniform low-bit quantization. It is faster than quantization-aware training from scratch and more accurate than methods aimed only at selecting thresholds and reducing noise from quantization. We also investigated quantization noise in neural networks for low-bit quantization and concluded that quantization noise is not always a good metric for quantization quality.",
author = "D. Chudakov and A. Goncharenko and S. Alyamkin and A. Densidov",
note = "Publisher Copyright: {\textcopyright} 2021 Institute of Physics Publishing. All rights reserved.; 8th International Young Scientists Conference on Information Technologies, Telecommunications and Control Systems, ITTCS 2021 ; Conference date: 16-12-2021 Through 17-12-2021",
year = "2021",
month = dec,
day = "20",
doi = "10.1088/1742-6596/2134/1/012004",
language = "English",
volume = "2134",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Quantization noise in low bit quantization and iterative adaptation to quantization noise in quantizable neural networks

AU - Chudakov, D.

AU - Goncharenko, A.

AU - Alyamkin, S.

AU - Densidov, A.

N1 - Publisher Copyright: © 2021 Institute of Physics Publishing. All rights reserved.

PY - 2021/12/20

Y1 - 2021/12/20

N2 - Quantization is one of the most popular and widely used methods of speeding up a neural network. At the moment, the standard is 8-bit uniform quantization. Nevertheless, the use of uniform low-bit quantization (4- and 6-bit quantization) has significant advantages in speed and resource requirements for inference. We present our quantization algorithm that offers advantages when using uniform low-bit quantization. It is faster than quantization-aware training from scratch and more accurate than methods aimed only at selecting thresholds and reducing noise from quantization. We also investigated quantization noise in neural networks for low-bit quantization and concluded that quantization noise is not always a good metric for quantization quality.

AB - Quantization is one of the most popular and widely used methods of speeding up a neural network. At the moment, the standard is 8-bit uniform quantization. Nevertheless, the use of uniform low-bit quantization (4- and 6-bit quantization) has significant advantages in speed and resource requirements for inference. We present our quantization algorithm that offers advantages when using uniform low-bit quantization. It is faster than quantization-aware training from scratch and more accurate than methods aimed only at selecting thresholds and reducing noise from quantization. We also investigated quantization noise in neural networks for low-bit quantization and concluded that quantization noise is not always a good metric for quantization quality.

UR - http://www.scopus.com/inward/record.url?scp=85123640836&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/2134/1/012004

DO - 10.1088/1742-6596/2134/1/012004

M3 - Conference article

AN - SCOPUS:85123640836

VL - 2134

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012004

T2 - 8th International Young Scientists Conference on Information Technologies, Telecommunications and Control Systems, ITTCS 2021

Y2 - 16 December 2021 through 17 December 2021

ER -

ID: 35377964