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Algorithm And Architecture Design Of Network On Chip Based Convolutional Neural Network Accelerator
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Book Synopsis On-Chip Training NPU - Algorithm, Architecture and SoC Design by : Donghyeon Han
Download or read book On-Chip Training NPU - Algorithm, Architecture and SoC Design written by Donghyeon Han and published by Springer Nature. This book was released on 2023-08-28 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlike most available sources that focus on deep neural network (DNN) inference, this book provides readers with a single-source reference on the needs, requirements, and challenges involved with on-device, DNN training semiconductor and SoC design. The authors include coverage of the trends and history surrounding the development of on-device DNN training, as well as on-device training semiconductors and SoC design examples to facilitate understanding.
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 Morgan & Claypool Publishers. This book was released on 2020-06-24 with total page 354 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 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 the 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 a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.
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 508 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Network-on-Chip Security and Privacy by : Prabhat Mishra
Download or read book Network-on-Chip Security and Privacy written by Prabhat Mishra and published by Springer Nature. This book was released on 2021-06-04 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage of Network-on-Chip (NoC) security vulnerabilities and state-of-the-art countermeasures, with contributions from System-on-Chip (SoC) designers, academic researchers and hardware security experts. Readers will gain a clear understanding of the existing security solutions for on-chip communication architectures and how they can be utilized effectively to design secure and trustworthy systems.
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
Book Synopsis Deep Learning on Edge Computing Devices by : Xichuan Zhou
Download or read book Deep Learning on Edge Computing Devices written by Xichuan Zhou and published by Elsevier. This book was released on 2022-02-02 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. - Focuses on hardware architecture and embedded deep learning, including neural networks - Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications - Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud - Describes how to maximize the performance of deep learning on Edge-computing devices - Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring
Book Synopsis Embedded Deep Learning by : Bert Moons
Download or read book Embedded Deep Learning written by Bert Moons and published by Springer. This book was released on 2018-10-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
Book Synopsis Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design by : Nan Zheng
Download or read book Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design written by Nan Zheng and published by John Wiley & Sons. This book was released on 2019-10-18 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.
Book Synopsis Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning, volume II by : Huajin Tang
Download or read book Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning, volume II written by Huajin Tang and published by Frontiers Media SA. This book was released on 2024-08-26 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Towards the long-standing dream of artificial intelligence, two solution paths have been paved: (i) neuroscience-driven neuromorphic computing; (ii) computer science-driven machine learning. The former targets at harnessing neuroscience to obtain insights for brain-like processing, by studying the detailed implementation of neural dynamics, circuits, coding and learning. Although our understanding of how the brain works is still very limited, this bio-plausible way offers an appealing promise for future general intelligence. In contrast, the latter aims at solving practical tasks typically formulated as a cost function with high accuracy, by eschewing most neuroscience details in favor of brute force optimization and feeding a large volume of data. With the help of big data (e.g. ImageNet), high-performance processors (e.g. GPU, TPU), effective training algorithms (e.g. artificial neural networks with gradient descent training), and easy-to-use design tools (e.g. Pytorch, Tensorflow), machine learning has achieved superior performance in a broad spectrum of scenarios. Although acclaimed for the biological plausibility and the low power advantage (benefit from the spike signals and event-driven processing), there are ongoing debates and skepticisms about neuromorphic computing since it usually performs worse than machine learning in practical tasks especially in terms of the accuracy.
Book Synopsis Hardware for Artificial Intelligence by : Alexantrou Serb
Download or read book Hardware for Artificial Intelligence written by Alexantrou Serb and published by Frontiers Media SA. This book was released on 2022-09-26 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Compact and Fast Machine Learning Accelerator for IoT Devices by : Hantao Huang
Download or read book Compact and Fast Machine Learning Accelerator for IoT Devices written by Hantao Huang and published by Springer. This book was released on 2018-12-07 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
Book Synopsis Low Power Circuit Design Using Advanced CMOS Technology by : Milin Zhang
Download or read book Low Power Circuit Design Using Advanced CMOS Technology written by Milin Zhang and published by CRC Press. This book was released on 2022-09-01 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: Low Power Circuit Design Using Advanced CMOS Technology is a summary of lectures from the first Advanced CMOS Technology Summer School (ACTS) 2017. The slides are selected from the handouts, while the text was edited according to the lecturers talk.ACTS is a joint activity supported by the IEEE Circuit and System Society (CASS) and the IEEE Solid-State Circuits Society (SSCS). The goal of the school is to provide society members as well researchers and engineers from industry the opportunity to learn about new emerging areas from leading experts in the field. ACTS is an example of high-level continuous education for junior engineers, teachers in academe, and students. ACTS was the results of a successful collaboration between societies, the local chapter leaders, and industry leaders. This summer school was the brainchild of Dr. Zhihua Wang, with strong support from volunteers from both the IEEE SSCS and CASS. In addition, the local companies, Synopsys China and Beijing IC Park, provided support.This first ACTS was held in the summer 2017 in Beijing. The lectures were given by academic researchers and industry experts, who presented each 6-hour long lectures on topics covering process technology, EDA skill, and circuit and layout design skills. The school was hosted and organized by the CASS Beijing Chapter, SSCS Beijing Chapter, and SSCS Tsinghua Student Chapter. The co-chairs of the first ACTS were Dr. Milin Zhang, Dr. Hanjun Jiang and Dr. Liyuan Liu. The first ACTS was a great success as illustrated by the many participants from all over China as well as by the publicity it has been received in various media outlets, including Xinhua News, one of the most popular news channels in China.
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.
Book Synopsis ICT with Intelligent Applications by : Jyoti Choudrie
Download or read book ICT with Intelligent Applications written by Jyoti Choudrie and published by Springer Nature. This book was released on 2022-09-30 with total page 827 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers papers addressing state-of-the-art research in all areas of information and communication technologies and their applications in intelligent computing, cloud storage, data mining and software analysis. It presents the outcomes of the Sixth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2022), held in Ahmedabad, India. The book is divided into two volumes. It discusses the fundamentals of various data analysis techniques and algorithms, making it a valuable resource for researchers and practitioners alike.
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.
Book Synopsis Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era by : Srinivasan, A.
Download or read book Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era written by Srinivasan, A. and published by IGI Global. This book was released on 2022-10-21 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, there has been an increasing interest in using machine learning and, in the last few years, deep learning methods combined with other vision and image processing techniques to create systems that solve vision problems in different fields. There is a need for academicians, developers, and industry-related researchers to present, share, and explore traditional and new areas of computer vision, machine learning, deep learning, and their combinations to solve problems. The Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era is designed to serve researchers and developers by sharing original, innovative, and state-of-the-art algorithms and architectures for applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, and more. It integrates the knowledge of the growing international community of researchers working on the application of machine learning and deep learning methods in vision and robotics. Covering topics such as brain tumor detection, heart disease prediction, and medical image detection, this premier reference source is an exceptional resource for medical professionals, faculty and students of higher education, business leaders and managers, librarians, government officials, researchers, and academicians.
Book Synopsis Advances in Machinery, Materials Science and Engineering Application IX by : M. Chen
Download or read book Advances in Machinery, Materials Science and Engineering Application IX written by M. Chen and published by IOS Press. This book was released on 2023-11-14 with total page 1172 pages. Available in PDF, EPUB and Kindle. Book excerpt: New engineering materials, techniques and applications are constantly being researched and developed, and keeping up to speed with the latest advances is crucial for engineers if they are to successfully address the challenges they face in their work. This book presents the selected proceedings of MMSE2023, the 9th International Conference on Advances in Machinery, Materials Science and Engineering Applications, jointly organized by the SAE-Supmeca, France and China University of Geosciences (Wuhan) and held on 22 and 23 July 2023 in Wuhan, China. For the past 12 years, this annual conference has collated recent advances and experiences, identified emerging trends and provided a platform for participants from academia and industry to exchange information and views, helping to address the world’s machinery and engineering challenges. The book contains 4 sections: mechanical engineering, material science and manufacturing technology; electrical engineering, automation and control; modeling, simulation and optimization techniques in engineering; and advanced engineering technologies and applications. A total of 241 submissions were received for MMSE2023, of which 151 papers were selected for the conference and for publication by means of a rigorous international peer-review process. These papers present exciting ideas and methods that will open novel research directions for different communities. Offering a current overview of the latest research and applications in machinery and materials-science engineering, the book will be of interest to all those working in the field.
