Fpga Based Multi Core Architectures For Deep Learning Networks

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Domain-Specific Computer Architectures for Emerging Applications

Author : Chao Wang
Publisher : CRC Press
ISBN 13 : 1040031986
Total Pages : 417 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Domain-Specific Computer Architectures for Emerging Applications by : Chao Wang

Download or read book Domain-Specific Computer Architectures for Emerging Applications written by Chao Wang and published by CRC Press. This book was released on 2024-06-04 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application. DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. This book places particular focus on accelerator hardware platforms and distributed systems for various novel applications, such as machine learning, data mining, neural networks, and graph algorithms, and also covers RISC-V open-source instruction sets. It briefly describes the system design methodology based on DSAs and presents the latest research results in academia around domain-specific acceleration architectures. Providing cutting-edge discussion of big data and artificial intelligence scenarios in contemporary industry and typical DSA applications, this book appeals to industry professionals as well as academicians researching the future of computing in these areas.

Number Systems for Deep Neural Network Architectures

Author : Ghada Alsuhli
Publisher : Springer Nature
ISBN 13 : 3031381335
Total Pages : 100 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Number Systems for Deep Neural Network Architectures by : Ghada Alsuhli

Download or read book Number Systems for Deep Neural Network Architectures written by Ghada Alsuhli and published by Springer Nature. This book was released on 2023-09-01 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers a comprehensive introduction to alternative number systems for more efficient representations of Deep Neural Network (DNN) data. Various number systems (conventional/unconventional) exploited for DNNs are discussed, including Floating Point (FP), Fixed Point (FXP), Logarithmic Number System (LNS), Residue Number System (RNS), Block Floating Point Number System (BFP), Dynamic Fixed-Point Number System (DFXP) and Posit Number System (PNS). The authors explore the impact of these number systems on the performance and hardware design of DNNs, highlighting the challenges associated with each number system and various solutions that are proposed for addressing them.

Deep In-memory Architectures for Machine Learning

Author : Mingu Kang
Publisher : Springer Nature
ISBN 13 : 3030359719
Total Pages : 181 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Deep In-memory Architectures for Machine Learning by : Mingu Kang

Download or read book Deep In-memory Architectures for Machine Learning written by Mingu Kang and published by Springer Nature. This book was released on 2020-01-30 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware.

Network and Parallel Computing

Author : Xin He
Publisher : Springer Nature
ISBN 13 : 3030794784
Total Pages : 480 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Network and Parallel Computing by : Xin He

Download or read book Network and Parallel Computing written by Xin He and published by Springer Nature. This book was released on 2021-06-22 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 17th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2020, held in Zhengzhou, China, in September 2020. The 34 full and 7 short papers presented in this volume were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: accelerator; AI; algorithm; architecture and hardware; big data and cloud; edge computing; emerging; network; and storage.

VLSI and Hardware Implementations using Modern Machine Learning Methods

Author : Sandeep Saini
Publisher : CRC Press
ISBN 13 : 1000523845
Total Pages : 292 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis VLSI and Hardware Implementations using Modern Machine Learning Methods by : Sandeep Saini

Download or read book VLSI and Hardware Implementations using Modern Machine Learning Methods written by Sandeep Saini and published by CRC Press. This book was released on 2021-12-31 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Multi-Processor System-on-Chip 1

Author : Liliana Andrade
Publisher : John Wiley & Sons
ISBN 13 : 1119818273
Total Pages : 320 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Multi-Processor System-on-Chip 1 by : Liliana Andrade

Download or read book Multi-Processor System-on-Chip 1 written by Liliana Andrade and published by John Wiley & Sons. This book was released on 2021-03-12 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Multi-Processor System-on-Chip (MPSoC) is the key component for complex applications. These applications put huge pressure on memory, communication devices and computing units. This book, presented in two volumes – Architectures and Applications – therefore celebrates the 20th anniversary of MPSoC, an interdisciplinary forum that focuses on multi-core and multi-processor hardware and software systems. It is this interdisciplinarity which has led to MPSoC bringing together experts in these fields from around the world, over the last two decades. Multi-Processor System-on-Chip 1 covers the key components of MPSoC: processors, memory, interconnect and interfaces. It describes advance features of these components and technologies to build efficient MPSoC architectures. All the main components are detailed: use of memory and their technology, communication support and consistency, and specific processor architectures for general purposes or for dedicated applications.

Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning

Author : Vikram Jain
Publisher : Springer Nature
ISBN 13 : 3031382307
Total Pages : 199 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning by : Vikram Jain

Download or read book Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning written by Vikram Jain and published by Springer Nature. This book was released on 2023-09-15 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores and motivates the need for building homogeneous and heterogeneous multi-core systems for machine learning to enable flexibility and energy-efficiency. Coverage focuses on a key aspect of the challenges of (extreme-)edge-computing, i.e., design of energy-efficient and flexible hardware architectures, and hardware-software co-optimization strategies to enable early design space exploration of hardware architectures. The authors investigate possible design solutions for building single-core specialized hardware accelerators for machine learning and motivates the need for building homogeneous and heterogeneous multi-core systems to enable flexibility and energy-efficiency. The advantages of scaling to heterogeneous multi-core systems are shown through the implementation of multiple test chips and architectural optimizations.

Machine Learning Applications in Electronic Design Automation

Author : Haoxing Ren
Publisher : Springer Nature
ISBN 13 : 303113074X
Total Pages : 585 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Machine Learning Applications in Electronic Design Automation by : Haoxing Ren

Download or read book Machine Learning Applications in Electronic Design Automation written by Haoxing Ren and published by Springer Nature. This book was released on 2023-01-01 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.

Smart Data

Author : Kuan-Ching Li
Publisher : CRC Press
ISBN 13 : 0429018037
Total Pages : 429 pages
Book Rating : 4.4/5 (29 download)

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Book Synopsis Smart Data by : Kuan-Ching Li

Download or read book Smart Data written by Kuan-Ching Li and published by CRC Press. This book was released on 2019-03-19 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest research trends in fields related to smart data computing and applications, including new computing theories, data mining and machine learning techniques. The book features contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning, Big Data capture and representation, AI for Big Data applications, and more. Features Presents state-of-the-art research in big data and smart computing Provides a broad coverage of topics in data science and machine learning Combines computing methods with domain knowledge and a focus on applications in science, engineering, and business Covers data security and privacy, including AI techniques Includes contributions from leading researchers

ICT for Intelligent Systems

Author : Jyoti Choudrie
Publisher : Springer Nature
ISBN 13 : 9819758106
Total Pages : 382 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis ICT for Intelligent Systems by : Jyoti Choudrie

Download or read book ICT for Intelligent Systems written by Jyoti Choudrie and published by Springer Nature. This book was released on with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Artificial Neural Networks and Machine Learning – ICANN 2016

Author : Alessandro E.P. Villa
Publisher : Springer
ISBN 13 : 3319447785
Total Pages : 585 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2016 by : Alessandro E.P. Villa

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2016 written by Alessandro E.P. Villa and published by Springer. This book was released on 2016-08-26 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set, LNCS 9886 + 9887, constitutes the proceedings of the 25th International Conference on Artificial Neural Networks, ICANN 2016, held in Barcelona, Spain, in September 2016. The 121 full papers included in this volume were carefully reviewed and selected from 227 submissions. They were organized in topical sections named: from neurons to networks; networks and dynamics; higher nervous functions; neuronal hardware; learning foundations; deep learning; classifications and forecasting; and recognition and navigation. There are 47 short paper abstracts that are included in the back matter of the volume.

Applied Computer Sciences in Engineering

Author : Juan Carlos Figueroa-García
Publisher : Springer Nature
ISBN 13 : 3031206118
Total Pages : 495 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Applied Computer Sciences in Engineering by : Juan Carlos Figueroa-García

Download or read book Applied Computer Sciences in Engineering written by Juan Carlos Figueroa-García and published by Springer Nature. This book was released on 2022-11-23 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 9th Workshop on Engineering Applications on Applied Computer Sciences in Engineering, WEA 2022, which took place in Bogotá, Colombia, in November/December 2022. The 39 papers presented in this volume were carefully reviewed and selected from 143 submissions. They were organized in topical sections as follows: Artificial Intelligence; Optimization; Simulation; and Applications.

Algorithms and Architectures for Parallel Processing

Author : Zahir Tari
Publisher : Springer Nature
ISBN 13 : 981970801X
Total Pages : 524 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Algorithms and Architectures for Parallel Processing by : Zahir Tari

Download or read book Algorithms and Architectures for Parallel Processing written by Zahir Tari and published by Springer Nature. This book was released on with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Advanced Computer Architecture

Author : Chao Li
Publisher : Springer
ISBN 13 : 9811324239
Total Pages : 238 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Advanced Computer Architecture by : Chao Li

Download or read book Advanced Computer Architecture written by Chao Li and published by Springer. This book was released on 2018-09-12 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th Annual Conference on Advanced Computer Architecture, ACA 2018, held in Yingkou, China, in August 2018. The 17 revised full papers presented were carefully reviewed and selected from 80 submissions. The papers of this volume are organized in topical sections on: accelerators; new design explorations; towards efficient ML/AI; parallel computing system.

Deep Learning: Convergence to Big Data Analytics

Author : Murad Khan
Publisher : Springer
ISBN 13 : 9811334595
Total Pages : 93 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Deep Learning: Convergence to Big Data Analytics by : Murad Khan

Download or read book Deep Learning: Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Spike-based learning application for neuromorphic engineering

Author : Anup Das
Publisher : Frontiers Media SA
ISBN 13 : 2832553184
Total Pages : 235 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Spike-based learning application for neuromorphic engineering by : Anup Das

Download or read book Spike-based learning application for neuromorphic engineering written by Anup Das and published by Frontiers Media SA. This book was released on 2024-08-22 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spiking Neural Networks (SNN) closely imitate biological networks. Information processing occurs in both spatial and temporal manner, making SNN extremely interesting for the pertinent mimicking of the biological brain. Biological brains code and transmit the sensory information in the form of spikes that capture the spatial and temporal information of the environment with amazing precision. This information is processed in an asynchronous way by the neural layer performing recognition of complex spatio-temporal patterns with sub-milliseconds delay and at with a power budget in the order of 20W. The efficient spike coding mechanism and the asynchronous and sparse processing and communication of spikes seems to be key in the energy efficiency and high-speed computation capabilities of biological brains. SNN low-power and event-based computation make them more attractive when compared to other artificial neural networks (ANN).