Neuro-inspired Computing for Next-gen AI: Computing Model, Architectures and Learning Algorithms PDF Download

Author: Angeliki Pantazi
Publisher: Frontiers Media SA
ISBN: 2889768562
Category : Science
Languages : en
Pages : 160

View

Book Description

Author: Chenwei Deng
Publisher: Frontiers Media SA
ISBN: 2832521169
Category : Science
Languages : en
Pages : 223

View

Book Description

Author: Robert Kozma
Publisher: Academic Press
ISBN: 0323958168
Category : Computers
Languages : en
Pages : 398

View

Book Description
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Author: Hong Qu
Publisher: Elsevier
ISBN: 0443328218
Category : Computers
Languages : en
Pages : 222

View

Book Description
Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications provides readers with in-depth understanding of Spiking Neural Networks (SNNs), which is a burgeoning research branch of Artificial Neural Networks (ANNs), AI, and Machine Learning that sits at the heart of the integration between Computer Science and Neural Engineering. In recent years, neural networks have re-emerged in relation to AI, representing a well-grounded paradigm rooted in disciplines from physics and psychology to information science and engineering.This book represents one of the established cross-over areas where neurophysiology, cognition, and neural engineering coincide with the development of new Machine Learning and AI paradigms. There are many excellent theoretical achievements in neuron models, learning algorithms, network architecture, and so on. But these achievements are numerous and scattered, with a lack of straightforward systematic integration, making it difficult for researchers to assimilate and apply. As the third generation of Artificial Neural Networks (ANNs), Spiking Neural Networks (SNNs) simulate the neuron dynamics and information transmission in a biological neural system in more detail, which is a cross-product of computer science and neuroscience. The primary target audience of this book is divided into two categories: artificial intelligence researchers who know nothing about SNNs, and researchers who know a lot about SNNs. The former needs to acquire fundamental knowledge of SNNs, but the challenge is that much of the existing literature on SNNs only slightly mentions the basic knowledge of SNNs, or is too superficial, and this book gives a systematic explanation from scratch. The latter needs learning about some novel research achievements in the field of SNNs, and this book introduces the latest research results on different aspects of SNNs and provides detailed simulation processes to facilitate readers' replication. In addition, the book introduces neuromorphic hardware architecture as a further extension of the SNN system.The book starts with the birth and development of SNNs, and then introduces the main research hotspots, including spiking neuron models, learning algorithms, network architectures, and neuromorphic hardware. Therefore, the book provides readers with easy access to both the foundational concepts and recent research findings in SNNs. - Introduces Spiking Neural Networks (SNNs), a new generation of biologically inspired artificial intelligence. - Systematically presents basic concepts of SNNs, neuron and network models, learning algorithms, and neuromorphic hardware. - Introduces the latest research results on various aspects of SNNs and provides detailed simulation processes to facilitate readers' replication.

Author: Albert Lee
Publisher:
ISBN:
Category :
Languages : en
Pages : 146

View

Book Description
In recent years, neural networks have contributed significantly to the advancement of machine learning, achieving state-of-the-art over a broad range of challenging tasks. The world right now is seeing a global artificial intelligence (AI) revolution involving academic and industry alike: tech giants like Google and Microsoft are applying machine learning in their commercial products, while professors from every discipline- computer science, engineering, mathematics, biology, transportation - scrambling to apply these methods to advance their research. Stock analysts are using AI to analyze and predict stock prices, medical experts to diagnose and develop new drugs, while game developers create sophisticated, human-like behavior in characters. At the national level, both NSF and DARPA have identified AI as one of the major national research directions. Our research targets the advancement of next-generation AI from three vertical aspects along the computing hierarchy: At the algorithm level, we propose the use of application-specific, bio-inspired neural networks for information processing. We develop models of specialized audio and visual neurons that are compatible with existing algorithms; and optimize neural architectures containing these neurons to understand their role in creating an efficient network. At the hardware level, we address the memory bottleneck in AI accelerators. We propose two schemes to overcome limitations caused by variation in critical path and fabrication processes. At the single device level, we recognize the significant performance gain from devices that compose AI computation via physical mechanisms. We propose two spintronic structures capable of computing convolutions that achieve orders of magnitude higher efficiency than state-of-the-art technology. These innovations provide the foundation for higher performance and more efficient AI at different temporal points throughout the coming decade: in the short term, algorithms that can be implemented immediately; in the mid-term, hardware designs that can be realized in a few years; and in the long term, new device technologies to be adopted as the fabric of AI computation.

Author: Sabina Spiga
Publisher: Woodhead Publishing
ISBN: 0081027877
Category : Technology & Engineering
Languages : en
Pages : 569

View

Book Description
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: Danuta Rutkowska
Publisher: Physica
ISBN: 379081802X
Category : Computers
Languages : en
Pages : 292

View

Book Description
The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. The role model for intelligent systems is the human mind. The influ ence of the human mind as a role model is clearly visible in the methodolo gies which have emerged, mainly during the past two decades, for the con ception, design and utilization of intelligent systems. At the center of these methodologies are fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). Collectively, these methodologies constitute what is called soft computing (SC). In this perspective, soft computing is basically a coalition of methodologies which collectively provide a body of concepts and techniques for automation of reasoning and decision-making in an environment of imprecision, uncertainty and partial truth.

Author: Punit Gupta
Publisher: CRC Press
ISBN: 1000542254
Category : Computers
Languages : en
Pages : 219

View

Book Description
Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 72

View

Book Description
This comprehensive guide dives into the fascinating world of Artificial Intelligence (AI) and its cutting-edge subfield, Generative AI. Designed for beginners and enthusiasts alike, it equips you with the knowledge and skills to navigate the complexities of machine learning and unlock the power of AI for advanced applications. Building a Strong Foundation The journey begins with mastering the fundamentals. You'll explore the different approaches to AI, delve into the history of this revolutionary field, and gain a solid understanding of various subfields like Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Delving into Machine Learning Machine learning, the core of AI's learning ability, takes center stage. You'll grasp the difference between supervised and unsupervised learning paradigms, discover popular algorithms like decision trees and neural networks, and learn the importance of data preparation for optimal model performance. Evaluation metrics become your tools to measure how effectively your models are learning. Unveiling the Power of Deep Learning Get ready to explore the intricate world of Deep Learning, a powerful subset of machine learning inspired by the human brain. Demystify neural networks, the building blocks of deep learning, and dive into specialized architectures like Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for handling sequential data. Deep learning frameworks become your allies, simplifying the process of building and deploying complex deep learning models. The Art of Machine Creation: Generative AI The book then shifts its focus to the transformative realm of Generative AI. Here, machines not only learn but create entirely new data. Explore different types of generative models, from autoregressive models to variational autoencoders, and witness their applications in text generation, image synthesis, and even music creation. A Deep Dive into Generative Adversarial Networks (GANs) Among generative models, Generative Adversarial Networks (GANs) have captured the imagination of researchers and the public alike. This chapter delves into the intriguing concept of GANs, where a generator model continuously strives to create realistic data while a discriminator model acts as a critic, ensuring the generated data is indistinguishable from real data. You'll explore the training process, the challenges of taming GANs, and best practices for achieving optimal results. Advanced Applications Across Domains The book then showcases the transformative potential of Generative AI across various domains. Witness the power of text generation with RNNs, explore the ethical considerations surrounding deepfakes, and discover how chatbots are revolutionizing communication. In the visual realm, delve into Deep Dream and Neural Style Transfer algorithms, and witness the creation of realistic images and videos with cutting-edge generative models. Mastering AI and Generative AI empowers you to not only understand these revolutionary technologies but also leverage them for advanced applications. As you embark on this journey, be prepared to unlock the boundless potential of machine creation and shape the future of AI.

Author: Alain Cappy
Publisher: John Wiley & Sons
ISBN: 1786304724
Category : Technology & Engineering
Languages : en
Pages : 240

View

Book Description
With the end of Moore's law and the emergence of new application needs such as those of the Internet of Things (IoT) or artificial intelligence (AI), neuro-inspired, or neuromorphic, information processing is attracting more and more attention from the scientific community. Its principle is to emulate in a simplified way the formidable machine to process information which is the brain, with neurons and artificial synapses organized in network. These networks can be software – and therefore implemented in the form of a computer program – but also hardware and produced by nanoelectronic circuits. The material path allows very low energy consumption, and the possibility of faithfully reproducing the shape and dynamics of the action potentials of living neurons (biomimetic approach) or even being up to a thousand times faster (high frequency approach). This path is promising and welcomed by the major manufacturers of nanoelectronics, as circuits can now today integrate several million neurons and artificial synapses.