› › Application Of Fpga To Real Time Machine Learning

Application of FPGA to Real‐Time Machine Learning

Author : Piotr Antonik

Publisher : Springer

Page : 187 pages

File Size : 17,88 MB

Release : 2018-05-18

Category : Science

ISBN : 3319910531

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Book Description

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.



VLSI and Hardware Implementations using Modern Machine Learning Methods

Author : Sandeep Saini

Publisher : CRC Press

Page : 292 pages

File Size : 34,20 MB

Release : 2021-12-31

Category : Technology & Engineering

ISBN : 1000523845

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Book Description

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.



Exploring Zynq Mpsoc

Author : Louise H Crockett

Publisher :

Page : 642 pages

File Size : 10,39 MB

Release : 2019-04-11

Category :

ISBN : 9780992978754

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Book Description

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.





Reservoir Computing

Author : Kohei Nakajima

Publisher : Springer Nature

Page : 463 pages

File Size : 46,18 MB

Release : 2021-08-05

Category : Computers

ISBN : 9811316872

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Book Description

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.



Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity

Author : Lobo, Victor

Publisher : IGI Global

Page : 292 pages

File Size : 18,92 MB

Release : 2022-06-24

Category : Computers

ISBN : 1799894320

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Book Description

The growth of innovative cyber threats, many based on metamorphosing techniques, has led to security breaches and the exposure of critical information in sites that were thought to be impenetrable. The consequences of these hacking actions were, inevitably, privacy violation, data corruption, or information leaking. Machine learning and data mining techniques have significant applications in the domains of privacy protection and cybersecurity, including intrusion detection, authentication, and website defacement detection, that can help to combat these breaches. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity provides machine and deep learning methods for analysis and characterization of events regarding privacy and anomaly detection as well as for establishing predictive models for cyber attacks or privacy violations. It provides case studies of the use of these techniques and discusses the expected future developments on privacy and cybersecurity applications. Covering topics such as behavior-based authentication, machine learning attacks, and privacy preservation, this book is a crucial resource for IT specialists, computer engineers, industry professionals, privacy specialists, security professionals, consultants, researchers, academicians, and students and educators of higher education.



Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Author :

Publisher : Academic Press

Page : 416 pages

File Size : 22,19 MB

Release : 2021-03-28

Category : Mathematics

ISBN : 0128231246

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Book Description

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



Explainable Machine Learning Models and Architectures

Author : Suman Lata Tripathi

Publisher : John Wiley & Sons

Page : 277 pages

File Size : 41,12 MB

Release : 2023-08-29

Category : Computers

ISBN : 139418655X

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Book Description

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.



Applied Reconfigurable Computing. Architectures, Tools, and Applications

Author : Lin Gan

Publisher : Springer Nature

Page : 207 pages

File Size : 14,52 MB

Release : 2022-10-27

Category : Computers

ISBN : 3031199839

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Book Description

This book constitutes the proceedings of the 18th International Symposium on Applied Reconfigurable Computing, ARC 2022, held as a virtual event, in September 2022. The 13 full papers presented in this volume were reviewed and selected from 16 submissions. The papers cover a broad spectrum of applications of reconfigurable computing, from driving assistance, data and graph processing acceleration, computer security to the societal relevant topic of supporting early diagnosis of Covid infectious conditions.



Applied Cryptography and Network Security Workshops

Author : Jianying Zhou

Publisher : Springer Nature

Page : 512 pages

File Size : 14,84 MB

Release : 2021-07-21

Category : Computers

ISBN : 3030816451

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Book Description

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.