000			05379nam a22004215i 4500
988			_aSpringer_Computer_2022
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005			20230102122030.0
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008			221008s2022 sz | s |||| 0|eng d
020			_a9783030925253
024	7		_a10.1007/978-3-030-92525-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.87 _b2022 EB
100	1		_aBen Abdallah, Abderazek _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684967
245	1	0	_aNeuromorphic Computing Principles and Organization _cby Abderazek Ben Abdallah, Khanh N. Dang
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XXI, 244 páginas) _b140 ilustraciones, 103 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_a1 Introduction to Neuromorphic Computing Systems -- 2 Neuromorphic System Design Fundamentals -- 3 Learning in Neuromorphic Systems -- 4 Emerging Memory Devices for Neuromorphic Systems -- 5 Communication Networks for Neuromorphic Systems -- 6 Fault-Tolerant Neuromorphic System Design -- 7 Reconfigurable Neuromorphic Computing System -- 8 Case Study: Real Hardware-Software Design of 3D-NoC-based Neuromorphic System -- 9 Survey of Neuromorphic Systems.
520			_aThis book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given. A particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain's three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well. Neuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.
650		7	_2embne _9678664 _aRedes neuronales artificiales
700	1		_aDang, Khanh N. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684968
776	0	8	_iPrinted edition: _z9783030925246
776	0	8	_iPrinted edition: _z9783030925260
776	0	8	_iPrinted edition: _z9783030925277
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-92525-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _eIG _zSI