Author: Randy L. ShimabukuroPublisher:
ISBN:
Category :
Languages : en
Pages : 280
Book Description
An Analog Memory for Artificial Neural Networks
Author: Randy L. ShimabukuroPublisher:
ISBN:
Category :
Languages : en
Pages : 280
Book Description
Analog Memory for Neural Networks
Author: Moulishankar ChandrasekaranPublisher:
ISBN:
Category :
Languages : en
Pages : 70
Book Description
Human Memory Modeled with Standard Analog and Digital Circuits
Author: John Robert BurgerPublisher: John Wiley & Sons
ISBN: 9780470464199
Category : Computers
Languages : en
Pages : 318
Book Description
Gain a new perspective on how the brain works and inspires new avenues for design in computer science and engineering This unique book is the first of its kind to introduce human memory and basic cognition in terms of physical circuits, beginning with the possibilities of ferroelectric behavior of neural membranes, moving to the logical properties of neural pulses recognized as solitons, and finally exploring the architecture of cognition itself. It encourages invention via the methodical study of brain theory, including electrically reversible neurons, neural networks, associative memory systems within the brain, neural state machines within associative memory, and reversible computers in general. These models use standard analog and digital circuits that, in contrast to models that include non-physical components, may be applied directly toward the goal of constructing a machine with artificial intelligence based on patterns of the brain. Writing from the circuits and systems perspective, the author reaches across specialized disciplines including neuroscience, psychology, and physics to achieve uncommon coverage of: Neural membranes Neural pulses and neural memory Circuits and systems for memorizing and recalling Dendritic processing and human learning Artificial learning in artificial neural networks The asset of reversibility in man and machine Electrically reversible nanoprocessors Reversible arithmetic Hamiltonian circuit finders Quantum versus classical Each chapter introduces and develops new material and ends with exercises for readers to put their skills into practice. Appendices are provided for non-experts who want a quick overview of brain anatomy, brain psychology, and brain scanning. The nature of this book, with its summaries of major bodies of knowledge, makes it a most valuable reference for professionals, researchers, and students with career goals in artificial intelligence, intelligent systems, neural networks, computer architecture, and neuroscience. A solutions manual is available for instructors; to obtain a copy please email the editorial department at [email protected].
Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design
Author: Nan ZhengPublisher: John Wiley & Sons
ISBN: 1119507391
Category : Computers
Languages : en
Pages : 296
Book Description
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.
VLSI Design of Neural Networks
Author: Ulrich RamacherPublisher: Springer Science & Business Media
ISBN: 1461539943
Category : Technology & Engineering
Languages : en
Pages : 346
Book Description
The early era of neural network hardware design (starting at 1985) was mainly technology driven. Designers used almost exclusively analog signal processing concepts for the recall mode. Learning was deemed not to cause a problem because the number of implementable synapses was still so low that the determination of weights and thresholds could be left to conventional computers. Instead, designers tried to directly map neural parallelity into hardware. The architectural concepts were accordingly simple and produced the so called interconnection problem which, in turn, made many engineers believe it could be solved by optical implementation in adequate fashion only. Furthermore, the inherent fault-tolerance and limited computation accuracy of neural networks were claimed to justify that little effort is to be spend on careful design, but most effort be put on technology issues. As a result, it was almost impossible to predict whether an electronic neural network would function in the way it was simulated to do. This limited the use of the first neuro-chips for further experimentation, not to mention that real-world applications called for much more synapses than could be implemented on a single chip at that time. Meanwhile matters have matured. It is recognized that isolated definition of the effort of analog multiplication, for instance, would be just as inappropriate on the part ofthe chip designer as determination of the weights by simulation, without allowing for the computing accuracy that can be achieved, on the part of the user.
A Fully Analog Computing-in-Memory Accelerator for Neural Network
Author: Artificial Neural Networks
Author: V. Rao VemuriPublisher:
ISBN:
Category : Computers
Languages : en
Pages : 168
Book Description
This volume provides an introduction to the field of artificial neural networks, and their role in the emerging field of neurocomputing, and the theoretical concepts that are the focus of current research. The genesis of this subject can be traced back to the 1940s, while present interest is due to recent developments in theoretical models, technologies, and algorithms. The papers selected for this volume were published primarily in IEEE journals.
2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
Author: IEEE StaffPublisher:
ISBN: 9781728133928
Category :
Languages : en
Pages :
Book Description
This Symposium explores emerging trends and novel ideas and concepts in the area of VLSI The Symposium covers a range of topics from VLSI circuits, systems and design methods to system level design and system on chip issues, to bringing VLSI experience to new areas and technologies Future design methodologies as well as new CAD tools to support them will also be the key topics ISVLSI 2019 highlights a special theme of Neuromoprhic Computing Over almost two decades the Symposium has been a unique forum promoting multidisciplinary research and new visionary approaches in the area of VLSI
Mixed Design of Integrated Circuits and Systems
Author: Andrzej NapieralskiPublisher: Springer Science & Business Media
ISBN: 1461556511
Category : Technology & Engineering
Languages : en
Pages : 236
Book Description
Very fast advances in IC technologies have brought new challenges into the physical design of integrated systems. The emphasis on system performance, in lately developed applications, requires timing and power constraints to be considered at each stage of physical design. The size of ICs is decreasing continuously, and the density of power dissipated in the circuits is growing rapidly. The first challenge is the Information Technology where new materials, devices, telecommunication and multimedia facilities are developed. The second one is the Biomedical Science and Biotechnology. The utilisation of bloodless surgery is possible now because of wide micro-sensors and micro-actuators application. Nowadays, the modern micro systems can be implanted directly into the human body and the medicine can be applied right in the proper time and place in the patient body. The low-power devices are being developed particularly for medical and space applications. This has created for designers in all scientific domains new possibilities which must be handed down to the future generations of designers. In this spirit, we organised the Fourth International Workshop "MIXED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS" in order to provide an international forum for discussion and the exchange of information on education, teaching experiences, training and technology transfer in the area of microelectronics and microsystems.
Learning on Silicon
Author: G. CauwenberghsPublisher: Springer Science & Business Media
ISBN: 9780792385554
Category : Technology & Engineering
Languages : en
Pages : 444
Book Description
Learning on Silicon combines models of adaptive information processing in the brain with advances in microelectronics technology and circuit design. The premise is to construct integrated systems not only loaded with sufficient computational power to handle demanding signal processing tasks in sensory perception and pattern recognition, but also capable of operating autonomously and robustly in unpredictable environments through mechanisms of adaptation and learning. This edited volume covers the spectrum of Learning on Silicon in five parts: adaptive sensory systems, neuromorphic learning, learning architectures, learning dynamics, and learning systems. The 18 chapters are documented with examples of fabricated systems, experimental results from silicon, and integrated applications ranging from adaptive optics to biomedical instrumentation. As the first comprehensive treatment on the subject, Learning on Silicon serves as a reference for beginners and experienced researchers alike. It provides excellent material for an advanced course, and a source of inspiration for continued research towards building intelligent adaptive machines.