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JCR Impact Factor: 0.700
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Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
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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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

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2022-Jun-16
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  4/2024 - 10
View TOC | « Previous Article | Next Article »

Floating Point Multiple-Precision Fused Multiply Add Architecture for Deep Learning Computation on Artix 7 FPGA Board

VINOTHENI, M. S. See more information about VINOTHENI, M. S. on SCOPUS See more information about VINOTHENI, M. S. on IEEExplore See more information about VINOTHENI, M. S. on Web of Science, JAWAHAR SENTHIL KUMAR, V. See more information about JAWAHAR SENTHIL KUMAR, V. on SCOPUS See more information about JAWAHAR SENTHIL KUMAR, V. on SCOPUS See more information about JAWAHAR SENTHIL KUMAR, V. on Web of Science
 
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Download PDF pdficon (1,558 KB) | Citation | Downloads: 42 | Views: 47

Author keywords
field programmable gate arrays, architecture, high performance computing, parallel processing, very large scale integration

References keywords
point(23), floating(23), precision(15), systems(12), multiply(10), design(10), unit(9), efficient(9), arithmetic(7), multiple(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2024-11-30
Volume 24, Issue 4, Year 2024, On page(s): 93 - 102
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.04010

Abstract
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Deep learning (DL) has become a transformative force in today's world revolutionizing industries. However, its success relies on high-precision arithmetic units, leading to the requirement of powerful high precision arithmetic design. Hence, this research proposes the multiple precision fused multiply add (MPFMA) architecture for profound computing-based applications. The proposed MPFMA architecture is capable of performing momentous tasks in every single clock cycle such as eight consecutive numbers of half precision (HP) operations, four numbers of concurrent single precision (SP) operations, two simultaneous double precision (DP) operations and single quadruple precision (QP) operations. The propounded architecture is implemented using Xilinx Vivado 2022.2 on Artix-7 FPGA Basys 3 board that demonstrates the functionality and attainment. From the observed results, it is inferred that the proposed framework offers 50% area curtail with the conventional FMA architecture, while still meeting the precision requirements for deep learning tasks. With an astoundingly low error rate of 0.013 % and an amazing accuracy rate of 99.987 %, the MPFMA in deep learning hardware not only enhances model performance but also contributes to energy conservation, making DL systems more sustainable and promising for the future of smart intelligence applications.


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