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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
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Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

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SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  3/2021 - 1
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 HIGHLY CITED PAPER 

Robust 2-bit Quantization of Weights in Neural Network Modeled by Laplacian Distribution

PERIC, Z. See more information about PERIC, Z. on SCOPUS See more information about PERIC, Z. on IEEExplore See more information about PERIC, Z. on Web of Science, DENIC, B. See more information about  DENIC, B. on SCOPUS See more information about  DENIC, B. on SCOPUS See more information about DENIC, B. on Web of Science, DINCIC, M. See more information about  DINCIC, M. on SCOPUS See more information about  DINCIC, M. on SCOPUS See more information about DINCIC, M. on Web of Science, NIKOLIC, J. See more information about NIKOLIC, J. on SCOPUS See more information about NIKOLIC, J. on SCOPUS See more information about NIKOLIC, J. on Web of Science
 
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Download PDF pdficon (1,299 KB) | Citation | Downloads: 1,259 | Views: 1,686

Author keywords
image classification, neural networks, quantization, signal to noise ratio, source coding

References keywords
neural(18), networks(14), learning(7), information(7), quantization(6), processing(6), systems(5), machine(5), deep(5), convolutional(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-08-31
Volume 21, Issue 3, Year 2021, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.03001
Web of Science Accession Number: 000691632000001
SCOPUS ID: 85114815185

Abstract
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Significant efforts are constantly involved in finding manners to decrease the number of bits required for quantization of neural network parameters. Although in addition to compression, in neural networks, the application of quantizer models that are robust to changes in the variance of input data is of great importance, to the best of authors knowledge, this topic has not been sufficiently researched so far. For that reason, in this paper we give preference to logarithmic companding scalar quantizer, which has shown the best robustness in high quality quantization of speech signals, modelled similarly as weights in neural networks, by Laplacian distribution. We explore its performance by performing the exact and asymptotic analysis for low resolution scenario with 2-bit quantization, where we draw firm conclusions about the usability of the exact performance analysis and design of our quantizer. Moreover, we provide a manner to increase the robustness of the quantizer we propose by involving additional adaptation of the key parameter. Theoretical and experimental results obtained by applying our quantizer in processing of neural network weights are very good matched, and, for that reason, we can expect that our proposal will find a way to practical implementation.


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Cited-By CrossRef

[1] Two Novel Non-Uniform Quantizers with Application in Post-Training Quantization, Perić, Zoran, Aleksić, Danijela, Nikolić, Jelena, Tomić, Stefan, Mathematics, ISSN 2227-7390, Issue 19, Volume 10, 2022.
Digital Object Identifier: 10.3390/math10193435
[CrossRef]

[2] Iterative Algorithm for Parameterization of Two-Region Piecewise Uniform Quantizer for the Laplacian Source, Nikolić, Jelena, Aleksić, Danijela, Perić, Zoran, Dinčić, Milan, Mathematics, ISSN 2227-7390, Issue 23, Volume 9, 2021.
Digital Object Identifier: 10.3390/math9233091
[CrossRef]

[3] Performance of Post-Training Two-Bits Uniform and Layer-Wise Uniform Quantization for MNIST Dataset from the Perspective of Support Region Choice, Tomić, Stefan, Nikolić, Jelena, Perić, Zoran, Aleksić, Danijela, Gao, Hao, Mathematical Problems in Engineering, ISSN 1563-5147, Issue , 2022.
Digital Object Identifier: 10.1155/2022/1463094
[CrossRef]

[4] Whether the Support Region of Three-Bit Uniform Quantizer Has a Strong Impact on Post-Training Quantization for MNIST Dataset?, Nikolić, Jelena, Perić, Zoran, Aleksić, Danijela, Tomić, Stefan, Jovanović, Aleksandra, Entropy, ISSN 1099-4300, Issue 12, Volume 23, 2021.
Digital Object Identifier: 10.3390/e23121699
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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