國立虎尾科技大學 |

Neuromorphic Computing Principles and Organization

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neuromorphic Computing Principles and Organization/ by Abderazek Ben Abdallah, Khanh N. Dang.
作者:	Ben Abdallah, Abderazek.
其他作者:	Dang, Khanh N.
面頁冊數:	XXI, 244 p. 140 illus., 103 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational Intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-92525-3
ISBN:	9783030925253

Neuromorphic Computing Principles and Organization
Ben Abdallah, Abderazek.

Neuromorphic Computing Principles and Organization[electronic resource] /by Abderazek Ben Abdallah, Khanh N. Dang. - 1st ed. 2022. - XXI, 244 p. 140 illus., 103 illus. in color.online resource.

1 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.

This 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.

ISBN: 9783030925253

Standard No.: 10.1007/978-3-030-92525-3doiSubjects--Topical Terms:

768837
Computational Intelligence.

LC Class. No.: TK7895.M5

Dewey Class. No.: 004.22

Neuromorphic Computing Principles and Organization
LDR:05048nam a22004095i 4500 001 1086842
003 DE-He213
005 20220531202400.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030925253 $9 978-3-030-92525-3
024 7 $a 10.1007/978-3-030-92525-3 $2 doi
035 $a 978-3-030-92525-3
050 4 $a TK7895.M5
050 4 $a QA76.9.A73
072 7 $a UYF $2 bicssc
072 7 $a COM011000 $2 bisacsh
072 7 $a UYF $2 thema
082 0 4 $a 004.22 $2 23
100 1 $a Ben Abdallah, Abderazek. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1077659
245 1 0 $a Neuromorphic Computing Principles and Organization $h [electronic resource] / $c by Abderazek Ben Abdallah, Khanh N. Dang.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XXI, 244 p. 140 illus., 103 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 1 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 $a This 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 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Database Management System. $3 1365724
650 1 4 $a Processor Architectures. $3 669787
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Database management. $3 557799
650 0 $a Computer architecture. $3 528145
650 0 $a Microprocessors. $3 632481
700 1 $a Dang, Khanh N. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1393699
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030925246
776 0 8 $i Printed edition: $z 9783030925260
776 0 8 $i Printed edition: $z 9783030925277
856 4 0 $u https://doi.org/10.1007/978-3-030-92525-3
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)