2020 IEEE Workshop on Signal Processing Systems (SiPS) PDF Download

Author: Nuno Roma
Publisher:
ISBN: 9781728180991
Category :
Languages : en
Pages :

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Author: Qilian Liang
Publisher: Springer Nature
ISBN: 9819912601
Category : Technology & Engineering
Languages : en
Pages : 376

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This book brings together papers presented at the 2021 International Conference on Communications, Signal Processing, and Systems, Changbaishan, China, July 23-24, 2022, which provides a venue to disseminate the latest developments and to discuss the interactions and links between these multidisciplinary fields. Spanning topics ranging from communications, signal processing and systems, this book is aimed at undergraduate and graduate students in Electrical Engineering, Computer Science and Mathematics, researchers and engineers from academia and industry as well as government employees (such as NSF, DOD and DOE).

Author: Brijesh Iyer
Publisher: Springer Nature
ISBN: 9811620083
Category : Technology & Engineering
Languages : en
Pages : 601

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This book is a collection of selected high-quality research papers presented at the International Conference on Computing in Engineering and Technology (ICCET 2021), organized by Dr. Babasaheb Ambedkar Technological University, Lonere, India, during January 30–31, 2021. Focusing on frontier topics and next-generation technologies, it presents original and innovative research from academics, scientists, students and engineers alike. The theme of the conference is Applied Information Processing System.

Author: Triwiyanto Triwiyanto
Publisher: Springer Nature
ISBN: 9819902487
Category : Technology & Engineering
Languages : en
Pages : 708

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This book presents high-quality peer-reviewed papers from the International Conference on Electronics, Biomedical Engineering, and Health Informatics (ICEBEHI) 2022 held at Surabaya, Indonesia, virtually. The contents are broadly divided into three parts: (a) Electronics, (b) Biomedical Engineering, and (c) Health Informatics. The major focus is on emerging technologies and their applications in the domain of biomedical engineering. It includes papers based on original theoretical, practical, and experimental simulations, development, applications, measurements, and testing. Featuring the latest advances in the field of biomedical engineering applications, this book serves as a definitive reference resource for researchers, professors, and practitioners interested in exploring advanced techniques in the fields of electronics, biomedical engineering, and health informatics. The applications and solutions discussed here provide excellent reference material for future product development.

Author: Alberto Bosio
Publisher: Springer Nature
ISBN: 303094705X
Category : Technology & Engineering
Languages : en
Pages : 541

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This book serves as a single-source reference to the latest advances in Approximate Computing (AxC), a promising technique for increasing performance or reducing the cost and power consumption of a computing system. The authors discuss the different AxC design and validation techniques, and their integration. They also describe real AxC applications, spanning from mobile to high performance computing and also safety-critical applications.

Author: Syed Mohsin Abbas
Publisher: Springer Nature
ISBN: 3031316630
Category : Computers
Languages : en
Pages : 157

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This book gives a detailed overview of a universal Maximum Likelihood (ML) decoding technique, known as Guessing Random Additive Noise Decoding (GRAND), has been introduced for short-length and high-rate linear block codes. The interest in short channel codes and the corresponding ML decoding algorithms has recently been reignited in both industry and academia due to emergence of applications with strict reliability and ultra-low latency requirements . A few of these applications include Machine-to-Machine (M2M) communication, augmented and virtual Reality, Intelligent Transportation Systems (ITS), the Internet of Things (IoTs), and Ultra-Reliable and Low Latency Communications (URLLC), which is an important use case for the 5G-NR standard. GRAND features both soft-input and hard-input variants. Moreover, there are traditional GRAND variants that can be used with any communication channel, and specialized GRAND variants that are developed for a specific communication channel. This book presents a detailed overview of these GRAND variants and their hardware architectures. The book is structured into four parts. Part 1 introduces linear block codes and the GRAND algorithm. Part 2 discusses the hardware architecture for traditional GRAND variants that can be applied to any underlying communication channel. Part 3 describes the hardware architectures for specialized GRAND variants developed for specific communication channels. Lastly, Part 4 provides an overview of recently proposed GRAND variants and their unique applications. This book is ideal for researchers or engineers looking to implement high-throughput and energy-efficient hardware for GRAND, as well as seasoned academics and graduate students interested in the topic of VLSI hardware architectures. Additionally, it can serve as reading material in graduate courses covering modern error correcting codes and Maximum Likelihood decoding for short codes.

Author: Dietmar Fey
Publisher: Springer Nature
ISBN: 303166146X
Category :
Languages : en
Pages : 368

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Author: Ole Richter
Publisher: University of Groningen
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 362

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Based upon the most advanced human-made technology on this planet, CMOS integrated circuit technology, this dissertation examines the design of hardware components and systems to establish a technological foundation for the application of future breakthroughs in the intersection of AI and neuroscience. Humans have long imagined machines, robots, and computers that learn and display intelligence akin to animals and themselves. To advance the development of these machines, specialised research in custom-built hardware designed for specific types of computation, which mirrors the structure of powerful biological nervous systems, is especially important. This dissertation is driven by the quest to harness biological and artificial neural principles to enhance the efficiency, adaptability, and intelligence of electronic neurosynaptic and neuromorphic hardware systems. It investigates the hardware design of bio-inspired neural components and their integration into more extensive scale and efficient chip architectures suitable for edge processing and near-sensor environments. Exploring all steps to the creation of a custom chip, this work selectively surveys and advances the state-of-the-art in bio-inspired mixed-signal subthreshold integrated design for neurosynaptic systems in a practical fashion. Further, it presents a novel asynchronous digital convolutional neuronal network processing pipeline integrated with a vision sensor for smart sensing. In conclusion, it sets forth a series of open challenges and future directions for the field, emphasizing the need for a robust, future-proof base for bio-inspired design and the potential of asynchronous stream processor architectures.

Author: Sandeep Saini
Publisher: CRC Press
ISBN: 1000523845
Category : Technology & Engineering
Languages : en
Pages : 292

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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: Ana Moita
Publisher: Springer Nature
ISBN: 3031670884
Category :
Languages : en
Pages : 163

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