Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms

Name: Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms
Author: Veljko Milutinovic|Nenad Mitic|Aleksandar Kartelj|Milos Kotlar
ISBN: 9781799883517

Veljko Milutinovic|Nenad Mitic|Aleksandar Kartelj|Milos Kotlar

Language: English

Published by IGI Global, 2022

1799883515 / 9781799883517

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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. The presents research of energy efficient accelerators for machine learning .

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Title: Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms
Author: Veljko Milutinovic|Nenad Mitic|Aleksandar Kartelj|Milos Kotlar
Publisher: IGI Global
Publication year: 2022
Condition: New
Binding: Soft cover
Language: English
ISBN 10: 1799883515
ISBN 13: 9781799883517

About the Author

Veljko Milutinović (1951) received his PhD from the University of Belgrade in Serbia, spent about a decade on various faculty positions in the USA (mostly at Purdue University and more recently at the University of Indiana in Bloomington), and was a co-designer of the DARPAs pioneering GaAs RISC microprocessor on 200MHz (about a decade before the first commercial effort on that same speed) and was a co-designer also of the related GaAs Systolic Array (with 4096 GaAs microprocessors). Later, for almost three decades, he taught and conducted research at the University of Belgrade in Serbia, for departments of EE, MATH, BA, and PHYS/CHEM. His research is mostly in data mining algorithms and dataflow computing, with the emphasis on mapping of data analytics algorithms onto fast energy efficient architectures. Most of his research was done in cooperation with industry (Intel, Fairchild, Honeywell, Maxeler, HP, IBM, NCR, RCA, etc.). For 20 of his edited books, publication forewords or other contributions were written by 20 different Nobel Laureates with whom he cooperated on his past industry sponsored projects. He published 40 books (mostly in the USA), he has over 100 papers in SCI journals (mostly in IEEE and ACM journals), and he presented invited talks at over 400 destinations worldwide. He has well over 1000 Thomson-Reuters WoS citations, well over 1000 Elsevier SCOPUS citations, and well over 5000 Google Scholar citations. His Google Scholar h index is equal to 40. He is a Life Fellow of the IEEE since 2003 and a Member of The Academy of Europe since 2011. He is a member of the Serbian National Academy of Engineering and a Foreign Member of the Montenegrin National Academy of Sciences and Arts.

Nenad Mitić is a full Professor at the Department of Computer Science, Faculty of Mathematics, University of Belgrade. He received his BSc, MSc and PhD from the University of Belgrade, Faculty of Mathematics. From 1983 to 1991 he was the system analyst on an IBM mainframe in Statistical Office of the Republic of Serbia, Belgrade. From 1991 till now he is with Faculty of Mathematics, Belgrade. His research interests cover areas of bioinformatics, data mining, big data, and functional programming.

Aleksandar Kartelj completed his PhD in Computer Science at Faculty of Mathematics in the year 2014. His research interests cover the areas of optimization, mathematical programming, and data mining. His publications are mostly related to metaheuristic optimization methods, data classification, and dimensionality reduction.

Miloš Kotlar received his B.Sc. (2016) and M.Sc. (2017) degrees in Electrical and Computer Engineering from the University of Belgrade, School of Electrical Engineering, Serbia. He is a Ph.D. candidate at the School of Electrical Engineering, University of Belgrade. His general research interests include implementation of energy efficient tensor implementations using the dataflow paradigm (FPGA and ASIC accelerators) and meta learning approaches for anomaly detection tasks.

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