1# Library to Find Best eBook for Read
Author : Huanglong Li
Release : 2022-02-21
Genre : Science
Kind : eBook
Book Rating : 604/5 ( reviews)
Book Synopsis Memristive Neuromorphics: Materials, Devices, Circuits, Architectures, Algorithms and their Co-Design by : Huanglong Li
Download or read book Memristive Neuromorphics: Materials, Devices, Circuits, Architectures, Algorithms and their Co-Design written by Huanglong Li. This book was released on 2022-02-21. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Sabina Spiga
Release : 2020-06-12
Genre : Technology & Engineering
Kind : eBook
Book Rating : 877/5 ( reviews)
Book Synopsis Memristive Devices for Brain-Inspired Computing by : Sabina Spiga
Download or read book Memristive Devices for Brain-Inspired Computing written by Sabina Spiga. This book was released on 2020-06-12. Available in PDF, EPUB and Kindle. Book excerpt: Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications—Computational Memory, Deep Learning, and Spiking Neural Networks reviews the latest in material and devices engineering for optimizing memristive devices beyond storage applications and toward brain-inspired computing. The book provides readers with an understanding of four key concepts, including materials and device aspects with a view of current materials systems and their remaining barriers, algorithmic aspects comprising basic concepts of neuroscience as well as various computing concepts, the circuits and architectures implementing those algorithms based on memristive technologies, and target applications, including brain-inspired computing, computational memory, and deep learning. This comprehensive book is suitable for an interdisciplinary audience, including materials scientists, physicists, electrical engineers, and computer scientists. Provides readers an overview of four key concepts in this emerging research topic including materials and device aspects, algorithmic aspects, circuits and architectures and target applications Covers a broad range of applications, including brain-inspired computing, computational memory, deep learning and spiking neural networks Includes perspectives from a wide range of disciplines, including materials science, electrical engineering and computing, providing a unique interdisciplinary look at the field
Author : Jordi Suñé
Release : 2020-04-09
Genre : Technology & Engineering
Kind : eBook
Book Rating : 769/5 ( reviews)
Book Synopsis Memristors for Neuromorphic Circuits and Artificial Intelligence Applications by : Jordi Suñé
Download or read book Memristors for Neuromorphic Circuits and Artificial Intelligence Applications written by Jordi Suñé. This book was released on 2020-04-09. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.
Author : Nan Zheng
Release : 2019-10-18
Genre : Computers
Kind : eBook
Book Rating : 405/5 ( reviews)
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. This book was released on 2019-10-18. 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.
Author : National Academies of Sciences Engineering and Medicine
Release : 2021-09-22
Genre :
Kind : eBook
Book Rating : 197/5 ( reviews)
Book Synopsis Frontiers in Memristive Materials for Neuromorphic Processing Applications by : National Academies of Sciences Engineering and Medicine
Download or read book Frontiers in Memristive Materials for Neuromorphic Processing Applications written by National Academies of Sciences Engineering and Medicine. This book was released on 2021-09-22. Available in PDF, EPUB and Kindle. Book excerpt: Current von Neumann style computing is energy inefficient and bandwidth limited as information is physically shuttled via electrons between processor, short term non-volatile memory, and long-term storage. Biologically inspired neuromorphic computing, with its inherent autonomous learning capabilities and much lower power requirements based on analog processing, is seen as an avenue for overcoming these limitations. The development of nanoelectronic memory resistors, or memristors, is essential to neuromorphic architectures as they allow logic-based elements for information processing to be combined directly with nonvolatile memory for efficient emulation of neurons and synapses found in the brain. Memristors are typically composed of a switchable material with nonlinear hysteretic behavior sandwiched between two conducting encoding elements. The design, dynamic control, scaling and fundamental understanding of these materials is essential for establishing memristive devices. To explore the state-of-the-art in the materials fundamentally underlying memristor technologies: their science, their mechanisms and their functional imperatives to realize neuromorphic computing machines, the National Academies of Sciences, Engineering, and Medicine's Board on Physics and Astronomy convened a workshop on February 28, 2020. This publication summarizes the presentation and discussion of the workshop.
