Application Of Fpga To Real Time Machine Learning

Author : Piotr Antonik
Publisher : Springer
Page : 171 pages
File Size : 53,8 Mb
Release : 2018-05-18
Category : Science
ISBN : 9783319910536

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Application of FPGA to Real‐Time Machine Learning by Piotr Antonik Pdf

This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

Author : Nicole Hemsoth,Timothy Prickett Morgan
Publisher : Next Platform Press
Page : 128 pages
File Size : 40,6 Mb
Release : 2017-01-16
Category : Electronic
ISBN : 0692835466

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FPGA Frontiers by Nicole Hemsoth,Timothy Prickett Morgan Pdf

While field programmable gate arrays (FPGAs) are certainly not new, their time to take the market by force did not fully arrive until 2016, at least for a new wave of applications in research, enterprise, and machine learning. With key acquisitions, highly publicized use cases of FPGAs at scale for real-world applications, and momentum to make programming these devices easier, FPGAs found the limelight-and that story is just beginning. Tracing the progression of FPGA use cases, technology developments, and market trends via the compute infrastructure analysis publication, The Next Platform, authors Nicole Hemsoth and Timothy Prickett Morgan pull together the last year in FPGA developments and offer a synthesized, holistic view of where the industry is heading-and where the new application areas will emerge. From the use of these devices in deep learning and machine learning, high performance computing (HPC), and enterprise applications, the range of FPGA acceleration is growing. In this 2017 edition of the book, readers will see the big picture for FPGAs in terms of past, present, and future and be armed with a sense of direction for new applications and innovations on the device and software sides.

Author : Louise H Crockett,David Northcote,Craig Ramsay
Publisher : Unknown
Page : 642 pages
File Size : 51,7 Mb
Release : 2019-04-11
Category : Electronic
ISBN : 0992978750

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Exploring Zynq Mpsoc by Louise H Crockett,David Northcote,Craig Ramsay Pdf

This book introduces the Zynq MPSoC (Multi-Processor System-on-Chip), an embedded device from Xilinx. The Zynq MPSoC combines a sophisticated processing system that includes ARM Cortex-A53 applications and ARM Cortex-R5 real-time processors, with FPGA programmable logic. As well as guiding the reader through the architecture of the device, design tools and methods are also covered in detail: both the conventional hardware/software co-design approach, and the newer software-defined methodology using Xilinx's SDx development environment. Featured aspects of Zynq MPSoC design include hardware and software development, multiprocessing, safety, security and platform management, and system booting. There are also special features on PYNQ, the Python-based framework for Zynq devices, and machine learning applications. This book should serve as a useful guide for those working with Zynq MPSoC, and equally as a reference for technical managers wishing to gain familiarity with the device and its associated design methodologies.

Author : Anonim
Publisher : Academic Press
Page : 416 pages
File Size : 49,5 Mb
Release : 2021-03-28
Category : Computers
ISBN : 9780128231241

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by Anonim Pdf

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance

Author : Juan Antonio Lossio-Ventura
Publisher : Springer Nature
Page : 366 pages
File Size : 49,5 Mb
Release : 2024-07-01
Category : Electronic
ISBN : 9783031636165

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Information Management and Big Data by Juan Antonio Lossio-Ventura Pdf

Author : Sandeep Saini,Kusum Lata,G.R. Sinha
Publisher : CRC Press
Page : 292 pages
File Size : 44,9 Mb
Release : 2021-12-31
Category : Technology & Engineering
ISBN : 9781000523843

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VLSI and Hardware Implementations using Modern Machine Learning Methods by Sandeep Saini,Kusum Lata,G.R. Sinha Pdf

Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.

Author : Suman Lata Tripathi,Mufti Mahmud
Publisher : John Wiley & Sons
Page : 277 pages
File Size : 54,8 Mb
Release : 2023-10-03
Category : Computers
ISBN : 9781394185849

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Explainable Machine Learning Models and Architectures by Suman Lata Tripathi,Mufti Mahmud Pdf

EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

Author : Kohei Nakajima,Ingo Fischer
Publisher : Springer Nature
Page : 463 pages
File Size : 53,6 Mb
Release : 2021-08-05
Category : Computers
ISBN : 9789811316876

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Reservoir Computing by Kohei Nakajima,Ingo Fischer Pdf

This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.

Author : Sawyer D. Campbell,Douglas H. Werner
Publisher : John Wiley & Sons
Page : 596 pages
File Size : 47,8 Mb
Release : 2023-09-26
Category : Technology & Engineering
ISBN : 9781119853893

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Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning by Sawyer D. Campbell,Douglas H. Werner Pdf

Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics. To aid in reader comprehension, each chapter contains 10-15 illustrations, including prototype photos, line graphs, and electric field plots. Contributed to by leading research groups in the field, sample topics covered in Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning include: Optical and photonic design, including generative machine learning for photonic design and inverse design of electromagnetic systems RF and antenna design, including artificial neural networks for parametric electromagnetic modeling and optimization and analysis of uniform and non-uniform antenna arrays Inverse scattering, target classification, and other applications, including deep learning for high contrast inverse scattering of electrically large structures Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning is a must-have resource on the topic for university faculty, graduate students, and engineers within the fields of electromagnetics, wireless communications, antenna/RF design, and photonics, as well as researchers at large defense contractors and government laboratories.

Author : L. S. Jayashree,G. Selvakumar
Publisher : Springer Nature
Page : 142 pages
File Size : 40,5 Mb
Release : 2020-04-16
Category : Business & Economics
ISBN : 9783030309459

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Getting Started with Enterprise Internet of Things: Design Approaches and Software Architecture Models by L. S. Jayashree,G. Selvakumar Pdf

This novel textbook introduces Enterprise Internet of Things from technology, management and business perspectives, carefully examining enterprise environments through the lens of modernization with the Internet of Things (IoT). It also includes detailed case studies to offer meaningful insights for readers from various disciplines and areas. The book analyzes the ways in which the technology could contribute to the enterprise world in terms of revenue and new business models, and addresses the strategies and principles involved in developing IoT solutions with software engineering practices such as DevOps and Micro services architecture principles. By doing so, it offers readers a clear overview of the power of Internet of Things in building next generation enterprise use cases. The book enables readers to understand the latest opportunities to create new business models in enterprises using the unprecedented level of device connectivity, and the wealth of data generated and information exchange among these devices. As such, it appeals to various user groups, such as engineers trying to solve problems in their own domains using Enterprise IoT, academics interested in gaining a better understanding of applications of IoT in large-scale enterprises, and researchers wanting to contribute to the ever-growing and complex area of IoT.

Author : Alan Pak Tao Lau,Faisal Nadeem Khan
Publisher : Academic Press
Page : 404 pages
File Size : 43,6 Mb
Release : 2022-02-10
Category : Technology & Engineering
ISBN : 9780323852289

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Machine Learning for Future Fiber-Optic Communication Systems by Alan Pak Tao Lau,Faisal Nadeem Khan Pdf

Machine Learning for Future Fiber-Optic Communication Systems provides a comprehensive and in-depth treatment of machine learning concepts and techniques applied to key areas within optical communications and networking, reflecting the state-of-the-art research and industrial practices. The book gives knowledge and insights into the role machine learning-based mechanisms will soon play in the future realization of intelligent optical network infrastructures that can manage and monitor themselves, diagnose and resolve problems, and provide intelligent and efficient services to the end users. With up-to-date coverage and extensive treatment of various important topics related to machine learning for fiber-optic communication systems, this book is an invaluable reference for photonics researchers and engineers. It is also a very suitable text for graduate students interested in ML-based signal processing and networking. Discusses the reasons behind the recent popularity of machine learning (ML) concepts in modern optical communication networks and the why/where/how ML can play a unique role Presents fundamental ML techniques like artificial neural networks (ANNs), support vector machines (SVMs), K-means clustering, expectation-maximization (EM) algorithm, principal component analysis (PCA), independent component analysis (ICA), reinforcement learning, and more Covers advanced deep learning (DL) methods such as deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) Individual chapters focus on ML applications in key areas of optical communications and networking

Author : Shubhabrata Datta,J. Paulo Davim
Publisher : Springer Nature
Page : 202 pages
File Size : 40,6 Mb
Release : 2021-07-24
Category : Technology & Engineering
ISBN : 9783030758479

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Machine Learning in Industry by Shubhabrata Datta,J. Paulo Davim Pdf

This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial problems. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems.

Author : Debasis Chaudhuri,Jan Harm Pretorius,Debashis Das,Sauvik Bal
Publisher : CRC Press
Page : 468 pages
File Size : 45,8 Mb
Release : 2024-05-23
Category : Computers
ISBN : 9781040052488

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International Conference on Security, Surveillance and Artificial Intelligence (ICSSAI-2023) by Debasis Chaudhuri,Jan Harm Pretorius,Debashis Das,Sauvik Bal Pdf

The International Conference on Security, Surveillance & Artificial Intelligence (ICSSAI2023) was held in West Bengal, India during December 1–2, 2023. The conference was organized by the Techno India University, one of the renowned universities in the state of West Bengal which is committed for generating, disseminating and preserving knowledge.

Author : Jianying Zhou,Chuadhry Mujeeb Ahmed,Lejla Batina,Sudipta Chattopadhyay,Olga Gadyatskaya,Chenglu Jin,Jingqiang Lin,Eleonora Losiouk,Bo Luo,Suryadipta Majumdar,Mihalis Maniatakos,Daisuke Mashima,Weizhi Meng,Stjepan Picek,Masaki Shimaoka,Chunhua Su,Cong Wang
Publisher : Springer Nature
Page : 512 pages
File Size : 51,8 Mb
Release : 2021-07-21
Category : Computers
ISBN : 9783030816452

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Applied Cryptography and Network Security Workshops by Jianying Zhou,Chuadhry Mujeeb Ahmed,Lejla Batina,Sudipta Chattopadhyay,Olga Gadyatskaya,Chenglu Jin,Jingqiang Lin,Eleonora Losiouk,Bo Luo,Suryadipta Majumdar,Mihalis Maniatakos,Daisuke Mashima,Weizhi Meng,Stjepan Picek,Masaki Shimaoka,Chunhua Su,Cong Wang Pdf

This book constitutes the proceedings of the satellite workshops held around the 19th International Conference on Applied Cryptography and Network Security, ACNS 2021, held in Kamakura, Japan, in June 2021. The 26 papers presented in this volume were carefully reviewed and selected from 49 submissions. They stem from the following workshops: AIBlock 2021: Third International Workshop on Application Intelligence and Blockchain Security AIHWS 2021: Second International Workshop on Artificial Intelligence in Hardware Security AIoTS 2021: Third International Workshop on Artificial Intelligence and Industrial IoT Security CIMSS 2021: First International Workshop on Critical Infrastructure and Manufacturing System Security Cloud S&P 2021: Third International Workshop on Cloud Security and Privacy SCI 2021: Second International Workshop on Secure Cryptographic Implementation SecMT 2021: Second International Workshop on Security in Mobile Technologies SiMLA 2021; Third International Workshop on Security in Machine Learning and its Applications Due to the Corona pandemic the workshop was held as a virtual event.

Author : Mahrishi, Mehul,Hiran, Kamal Kant,Meena, Gaurav,Sharma, Paawan
Publisher : IGI Global
Page : 344 pages
File Size : 45,6 Mb
Release : 2020-04-24
Category : Computers
ISBN : 9781799830979

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Machine Learning and Deep Learning in Real-Time Applications by Mahrishi, Mehul,Hiran, Kamal Kant,Meena, Gaurav,Sharma, Paawan Pdf

Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.