Design And Performance Analysis Of Hardware Accelerator For Deep Neural Network In Heterogeneous Platform

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High Performance Computing for Big Data

Author : Chao Wang
Publisher : CRC Press
ISBN 13 : 1498784003
Total Pages : 287 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis High Performance Computing for Big Data by : Chao Wang

Download or read book High Performance Computing for Big Data written by Chao Wang and published by CRC Press. This book was released on 2017-10-16 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Author :
Publisher : Academic Press
ISBN 13 : 0128231246
Total Pages : 416 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by :

Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by and published by Academic Press. This book was released on 2021-03-28 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

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.

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Author : Shiho Kim
Publisher : Elsevier
ISBN 13 : 0128231238
Total Pages : 414 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by : Shiho Kim

Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by Shiho Kim and published by Elsevier. This book was released on 2021-04-07 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Smart Data

Author : Kuan-Ching Li
Publisher : CRC Press
ISBN 13 : 0429018029
Total Pages : 473 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 473 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

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.

Proceeding of 2022 International Conference on Wireless Communications, Networking and Applications (WCNA 2022)

Author : Zhihong Qian
Publisher : Springer Nature
ISBN 13 : 9819939518
Total Pages : 849 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Proceeding of 2022 International Conference on Wireless Communications, Networking and Applications (WCNA 2022) by : Zhihong Qian

Download or read book Proceeding of 2022 International Conference on Wireless Communications, Networking and Applications (WCNA 2022) written by Zhihong Qian and published by Springer Nature. This book was released on 2023-07-26 with total page 849 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings includes original, unpublished, peer-reviewed research papers from the International Conference on Wireless Communications, Networking and Applications (WCNA2022), held in Wuhan, Hubei, China, from December 16 to 18, 2022. The topics covered include but are not limited to wireless communications, networking and applications. The papers showcased here share the latest findings on methodologies, algorithms and applications in communication and network, making the book a valuable asset for professors, researchers, engineers, and university students alike.

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.

2018 IEEE ACM International Conference on Computer Aided Design (ICCAD)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781538675021
Total Pages : pages
Book Rating : 4.6/5 (75 download)

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Book Synopsis 2018 IEEE ACM International Conference on Computer Aided Design (ICCAD) by : IEEE Staff

Download or read book 2018 IEEE ACM International Conference on Computer Aided Design (ICCAD) written by IEEE Staff and published by . This book was released on 2018-11-05 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ICCAD serves EDA and design professionals, highlighting new challenges and innovative solutions for integrated circuit design technology and systems

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI

Author : Jeffrey Nichols
Publisher : Springer Nature
ISBN 13 : 3030633934
Total Pages : 555 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI by : Jeffrey Nichols

Download or read book Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI written by Jeffrey Nichols and published by Springer Nature. This book was released on 2020-12-22 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.

Neuromorphic Computing and Beyond

Author : Khaled Salah Mohamed
Publisher : Springer Nature
ISBN 13 : 3030372243
Total Pages : 241 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Neuromorphic Computing and Beyond by : Khaled Salah Mohamed

Download or read book Neuromorphic Computing and Beyond written by Khaled Salah Mohamed and published by Springer Nature. This book was released on 2020-01-25 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses and compares several new trends that can be used to overcome Moore’s law limitations, including Neuromorphic, Approximate, Parallel, In Memory, and Quantum Computing. The author shows how these paradigms are used to enhance computing capability as developers face the practical and physical limitations of scaling, while the demand for computing power keeps increasing. The discussion includes a state-of-the-art overview and the essential details of each of these paradigms.

Heterogeneous SoC Design and Verification

Author : Khaled Salah Mohamed
Publisher : Springer Nature
ISBN 13 : 303156152X
Total Pages : 177 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Heterogeneous SoC Design and Verification by : Khaled Salah Mohamed

Download or read book Heterogeneous SoC Design and Verification written by Khaled Salah Mohamed and published by Springer Nature. This book was released on with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data Orchestration in Deep Learning Accelerators

Author : Tushar Krishna
Publisher : Springer Nature
ISBN 13 : 3031017676
Total Pages : 158 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Data Orchestration in Deep Learning Accelerators by : Tushar Krishna

Download or read book Data Orchestration in Deep Learning Accelerators written by Tushar Krishna and published by Springer Nature. This book was released on 2022-05-31 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.

Neuromorphic Computing

Author :
Publisher : BoD – Books on Demand
ISBN 13 : 1803561432
Total Pages : 298 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis Neuromorphic Computing by :

Download or read book Neuromorphic Computing written by and published by BoD – Books on Demand. This book was released on 2023-11-15 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the cutting-edge world of Neuromorphic Computing, a groundbreaking volume that unravels the secrets of brain-inspired computational paradigms. Spanning neuroscience, artificial intelligence, and hardware design, this book presents a comprehensive exploration of neuromorphic systems, empowering both experts and newcomers to embrace the limitless potential of brain-inspired computing. Discover the fundamental principles that underpin neural computation as we journey through the origins of neuromorphic architectures, meticulously crafted to mimic the brain’s intricate neural networks. Unlock the true essence of learning mechanisms – unsupervised, supervised, and reinforcement learning – and witness how these innovations are shaping the future of artificial intelligence.

Optimization and Learning

Author : Bernabé Dorronsoro
Publisher : Springer Nature
ISBN 13 : 3031220390
Total Pages : 259 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Optimization and Learning by : Bernabé Dorronsoro

Download or read book Optimization and Learning written by Bernabé Dorronsoro and published by Springer Nature. This book was released on 2022-12-10 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Optimization and Learning, OLA 2022, which took place in Syracuse, Sicilia, Italy, in July 2022. The 19 full papers presented in this volume were carefully reviewed and selected from 52 submissions. The papers are organized in the following topical sections: Optimization and Learning; Novel Optimization Techniques; Logistics; and Applications.

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

Author : Ning Xiong
Publisher : Springer Nature
ISBN 13 : 3031207386
Total Pages : 1527 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery by : Ning Xiong

Download or read book Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery written by Ning Xiong and published by Springer Nature. This book was released on 2023-01-29 with total page 1527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book consists of papers on the recent progresses in the state of the art in natural computation, fuzzy systems, and knowledge discovery. The book is useful for researchers, including professors, graduate students, as well as R & D staff in the industry, with a general interest in natural computation, fuzzy systems, and knowledge discovery. The work printed in this book was presented at the 2022 18th International Conference on Natural Computation, Fuzzy Systems, and Knowledge Discovery (ICNC-FSKD 2022), held from 30 July to 1 August 2022, in Fuzhou, China. All papers were rigorously peer-reviewed by experts in the areas.

Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

Author : Sudeep Pasricha
Publisher : Springer Nature
ISBN 13 : 3031399323
Total Pages : 481 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing by : Sudeep Pasricha

Download or read book Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-10-09 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.