Volume 1 Machine Learning under Resource Constraints - Fundamentals

book: Volume 1 Machine Learning under Resource Constraints - Fundamentals

Open Access Published by De Gruyter 2023

Volume 1 Machine Learning under Resource Constraints - Fundamentals

Edited by: Katharina Morik and Peter Marwedel

Part of the multi-volume work Machine Learning under Resource Constraints

In the series De Gruyter STEM

https://doi.org/10.1515/9783110785944

Overview
Contents

About this book

Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.

Ranges from embedded systems to large computing clusters.
Provides application of the methods in various domains of science and engineering.

Author / Editor information

Katharina Morik received her doctorate from the University of Hamburg in 1981 and her habilitation from the TU Berlin in 1988. In 1991, she established the chair of Artificial Intelligence at the TU Dortmund University. She is a pioneer of machine learning contributing substantially to inductive logic programming, support vector machines, probabilistic graphical models. In 2011, she acquired the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained Data Analysis", of which she is the spokesperson. and computing architectures together so that machine learning models may be executed or even trained on resource restricted devices. It consists of 12 projects and a graduate school for more than 50 Ph. D. students. She is a spokesperson of the Competence Center for Machine Learning Rhein Ruhr (ML2R) and coordinator of the German competence centers for AI. She is the author of more than 200 publications in prestigious journals and conferences. She was a founding member, Program Chair and Vice Chair of the conference IEEE International Conference on Data Mining (ICDM) and is a member of the steering committee of and was Program Chair of ECML PKDD. Together with Volker Markl, Katharina Morik heads the working group "Technological Pioneers" of the platform "Learning Systems and Data Science" of the BMBF. Prof. Morik has been a member of the Academy of Technical Sciences since 2015 and of the North Rhine-Westphalian Academy of Sciences and Arts since 2016. She has been awarded Fellow of the German Society of Computer Science GI e.V. in 2019.

Dr. Peter Marwedel studied physics at the University of Kiel, Germany. He received his PhD in physics in 1974. As a post-doc, he published some of the first papers on high-level synthesis and retargetable compilation in the context of the MIMOLA hardware description language. In 1987, his habilitation thesis in computer science was accepted. He worked as a professor for computer engineering at TU Dortmund since 1989. He is chairing ICD, a local spin-off of TU Dortmund. His research interests include design automation for embedded systems, in particular the generation of efficient embedded software. Focus is on energy efficiency and timing predictability. Dr. Marwedel published papers on energy-efficient and timing-predictable software, including compiler-supported use of scratchpad memories. He is the author of one of the few textbooks on embedded systems. The book is complemented by videos available on youtube and by publicly available slides. He served as the vice-chair of the collaborative research center SFB 876, aiming at resource-efficient analysis of large data sets since 2011. Dr. Marwedel is an IEEE Fellow. He received the EDAA Lifetime Achievement Award in 2013 and the ESWEEK Lifetime achievement award in 2014.

Topics

Frontmatter Publicly Available Download PDF	I
Contents Publicly Available Download PDF	VII
Preface Publicly Available Download PDF	XI
1 Introduction Katharina Morik and Jian-Jia Chen Open Access Download PDF	1
Data Gathering and Resource Measuring Christoph Borchert, Jochen Streicher, Alexander Lochmann, Olaf Spinczyk, Mojtaba Masoudinejad, Markus Buschhoff, Andres Gomez, Lars Suter and Simon Mayer Open Access Download PDF	15
3 Streaming Data, Small Devices Sebastian Buschjäger, Katharina Morik and Alexander Munteanu Open Access Download PDF	71
4 Structured Data Nico Piatkowski, Katharina Morik, Nils Kriege, Christopher Morris, Matthias Fey, Frank Weichert, Nico Bertram, Jonas Ellert, Johannes Fischer and Lukas Pfahler Open Access Download PDF	99
5 Cluster Analysis Lars Lenssen, Erich Schubert, Amer Krivošija, Erich Schubert, Andreas Lang and Sibylle Hess Open Access Download PDF	179
6 Hardware-Aware Execution Wayne Luk, Ce Guo, Henning Funke, Jens Teubner, Erik Schultheis, Rohit Babbar, Helena Kotthaus and Peter Marwedel Open Access Download PDF	249
7 Memory Awareness Helena Kotthaus, Peter Marwedel, Mikail Yayla, Sebastian Buschjäger, Hussam Amrouch and Kuan-Hsun Chen Open Access Download PDF	305
8 Communication Awareness Kuan-Hsun Chen, Junjie Shi, Henning Funke and Jens Teubner Open Access Download PDF	359
9 Energy Awareness Nico Piatkowski and Robert Falkenberg Open Access Download PDF	405
Bibliography Open Access Download PDF	437
Index Open Access Download PDF	485
List of Contributors Open Access Download PDF	489

(Deutsch)

Language: English
Publisher: De Gruyter
Audience: Computer Scientists, Physicists, Engineers, Libraries.
Pages
- Front matter: 14
- Main content: 491
Illustrations
- Illustrations: 200
- Coloured Illustrations: 100
- Tables: 50
- Coloured Tables: 50
Keywords: Resource-Constrained Data Analysis; Resource-Aware Machine Learning; Embedded Systems and Machine Learning; Big Data and Machine Learning; Artificial Intelligence; Highly Distributed Data; ML on Small devices; Data mining for Ubiquitous System Software; Cyber-physical systems; Machine learning in high-energy physics; Machine learning for knowledge discovery

eBook

Published: December 31, 2022
ISBN: 9783110785944

Paperback

Published: December 31, 2022
ISBN: 9783110785937