Hardware Aware Probabilistic Machine Learning Models
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Hardware Aware Probabilistic Machine Learning Models
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Author : Laura Isabel Galindez Olascoaga
language : en
Publisher: Springer Nature
Release Date : 2021-05-19
Hardware Aware Probabilistic Machine Learning Models written by Laura Isabel Galindez Olascoaga and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-19 with Technology & Engineering categories.
This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
Iot Streams For Data Driven Predictive Maintenance And Iot Edge And Mobile For Embedded Machine Learning
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Author : Joao Gama
language : en
Publisher: Springer Nature
Release Date : 2021-01-09
Iot Streams For Data Driven Predictive Maintenance And Iot Edge And Mobile For Embedded Machine Learning written by Joao Gama and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-09 with Computers categories.
This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.
Efficient Execution Of Irregular Dataflow Graphs
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Author : Nimish Shah
language : en
Publisher: Springer Nature
Release Date : 2023-08-14
Efficient Execution Of Irregular Dataflow Graphs written by Nimish Shah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-14 with Technology & Engineering categories.
This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.
Advances In Intelligent Data Analysis Xviii
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Author : Michael R. Berthold
language : en
Publisher: Springer Nature
Release Date : 2020-04-22
Advances In Intelligent Data Analysis Xviii written by Michael R. Berthold and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-22 with Computers categories.
This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.
Machine Learning Under Resource Constraints Fundamentals
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Author : Katharina Morik
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2022-12-31
Machine Learning Under Resource Constraints Fundamentals written by Katharina Morik and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-31 with Science categories.
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.
Computational Intelligence For Green Cloud Computing And Digital Waste Management
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Author : Kumar, K. Dinesh
language : en
Publisher: IGI Global
Release Date : 2024-02-27
Computational Intelligence For Green Cloud Computing And Digital Waste Management written by Kumar, K. Dinesh and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-27 with Computers categories.
In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warming. Studies show that data centers alone are responsible for nearly eighty million metric tons of CO2 emissions worldwide, and this figure is poised to skyrocket to a staggering 8000 TWh by 2030 unless we revolutionize our approach to computing resource management. The root of this problem lies in inefficient resource allocation within cloud environments, as service providers often over-provision computing resources to avoid Service Level Agreement (SLA) violations, leading to both underutilization of resources and a significant increase in energy consumption. Computational Intelligence for Green Cloud Computing and Digital Waste Management stands as a beacon of hope in the face of the environmental and technological challenges we face. It introduces the concept of green computing, dedicated to creating an eco-friendly computing environment. The book explores innovative, intelligent resource management methods that can significantly reduce the power consumption of data centers. From machine learning and deep learning solutions to green virtualization technologies, this comprehensive guide explores innovative approaches to address the pressing challenges of green computing. Whether you are an educator teaching about green computing, an environmentalist seeking sustainability solutions, an industry professional navigating the digital landscape, a resolute researcher, or simply someone intrigued by the intersection of technology and sustainability, this book offers an indispensable resource.
Aspect Oriented Programming In Practice
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-31
Aspect Oriented Programming In Practice written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-31 with Computers categories.
"Aspect-Oriented Programming in Practice" "Aspect-Oriented Programming in Practice" offers a comprehensive and authoritative exploration of aspect-oriented programming (AOP) as a paradigm that revolutionizes software modularity and crosscutting concern management. Beginning with the historical motivations and conceptual underpinnings of AOP, the book clarifies its core terminology, such as join points, pointcuts, advice, and weaving. It contrasts AOP with object-oriented and functional programming, while delving into the real-world challenges, controversies, and the diverse landscape of crosscutting concerns, from logging and security to error handling and transaction management. The book then provides an in-depth technical tour of the primary languages, frameworks, and tools supporting AOP, including AspectJ, Spring AOP, and .NET solutions, alongside dynamic approaches for languages like Python and JavaScript. With practical guidelines on identifying crosscutting logic, designing reusable and efficient aspects, and weaving them into both new and legacy systems, readers gain actionable insight into aspect development, integration strategies, and advanced implementation patterns. Crucially, the text dedicates substantial attention to ensuring robustness, including extensive discussion of testing, debugging, validation, and performance optimization for aspect-enabled codebases. Looking beyond foundational practice, "Aspect-Oriented Programming in Practice" addresses architectural patterns, maintainability concerns, and the operational realities of scaling AOP in modern software ecosystems, such as microservices, cloud-native platforms, and reactive environments. It culminates in a review of evolving research, future directions, and open challenges facing the field—covering formal verification, self-adaptive systems, and the intersection of AOP with cutting-edge domains like machine learning. Rigorous yet accessible, this book is an indispensable resource for practitioners, architects, and researchers aiming to master aspect-oriented techniques and their diverse applications across contemporary software development.
Unlocking Artificial Intelligence
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Author : Christopher Mutschler
language : en
Publisher: Springer Nature
Release Date : 2024-07-29
Unlocking Artificial Intelligence written by Christopher Mutschler and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-29 with Mathematics categories.
This open access book provides a state-of-the-art overview of current machine learning research and its exploitation in various application areas. It has become apparent that the deep integration of artificial intelligence (AI) methods in products and services is essential for companies to stay competitive. The use of AI allows large volumes of data to be analyzed, patterns and trends to be identified, and well-founded decisions to be made on an informative basis. It also enables the optimization of workflows, the automation of processes and the development of new services, thus creating potential for new business models and significant competitive advantages. The book is divided in two main parts: First, in a theoretically oriented part, various AI/ML-related approaches like automated machine learning, sequence-based learning, deep learning, learning from experience and data, and process-aware learning are explained. In a second part, various applications are presented that benefit from the exploitation of recent research results. These include autonomous systems, indoor localization, medical applications, energy supply and networks, logistics networks, traffic control, image processing, and IoT applications. Overall, the book offers professionals and applied researchers an excellent overview of current exploitations, approaches, and challenges of AI/ML-related research.
Deep Learning Systems
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Author : Andres Rodriguez
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Deep Learning Systems written by Andres Rodriguez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to bettercollaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
Digital Personality
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Author : Kuldeep Singh Kaswan
language : en
Publisher: CRC Press
Release Date : 2024-08-30
Digital Personality written by Kuldeep Singh Kaswan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
A computer that imbibes human characteristics is considered to have a digital personality. The character is akin to real-life human with his/her distinguishing characteristics such as history, morality, beliefs, abilities, looks, and sociocultural embeddings. It also contains stable personality characteristics; fluctuating emotional, cognitive, SOAR technology, and motivational states. Digital Personality focuses on the creation of systems and interfaces that can observe, sense, predict, adapt to, affect, comprehend, or simulate the following: character based on behavior and situation, behavior based on character and situation, or situation based on character and behavior. Character sensing and profiling, character-aware adaptive systems, and artificial characters are the three primary subfields in digital personality. Digital Personality has attracted the interest of academics from a wide range of disciplines, including psychology, human-computer interaction, and character modeling. It is expected to expand quickly as technology and computer systems become more and more intertwined into our daily lives. Digital Personality is expected to draw at least as much attention as Affective Computing. The goal of affective computing is to enable computers to comprehend both spoken and nonverbal messages from people, use implicit body language, gaze, speech tones, and facial expressions, etc. to infer the emotional state and then reply appropriately or even show affect through interaction modalities. More natural and seamless human-computer connection would be the larger objective. Users will benefit from a more individualized experience as a result. Additionally, this will affect how well the user performs since they will have the assistance of the robots to do their jobs quickly and effectively. This book provides an overview of the character dimensions and how technology is aiding this area of study. It offers a fresh portrayal of character from several angles. It also discusses the applications of this new field of study.