Download or read book Space Time Computing with Temporal Neural Networks written by James E. Smith and published by Springer Nature. This book was released on 2022-05-31 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable. This book, written from the perspective of a computer designer and targeted at computer researchers, is intended to give both background and lay out a course of action for studying the brain's computational paradigm. It contains a mix of concepts and ideas drawn from computational neuroscience, combined with those of the author. As background, relevant biological features are described in terms of their computational and communication properties. The brain's neocortex is constructed of massively interconnected neurons that compute and communicate via voltage spikes, and a strong argument can be made that precise spike timing is an essential element of the paradigm. Drawing from the biological features, a mathematics-based computational paradigm is constructed. The key feature is spiking neurons that perform communication and processing in space-time, with emphasis on time. In these paradigms, time is used as a freely available resource for both communication and computation. Neuron models are first discussed in general, and one is chosen for detailed development. Using the model, single-neuron computation is first explored. Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Individual neurons are building blocks for constructing larger ensembles, referred to as "columns". These columns are trained in an unsupervised manner and operate collectively to perform the basic cognitive function of pattern clustering. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters. Larger cognitive systems are formed by combining columns into a hierarchical architecture. These higher level architectures are the subject of ongoing study, and progress to date is described in detail in later chapters. Simulation plays a major role in model development, and the simulation infrastructure developed by the author is described.

Download or read book Space Time Computing with Temporal Neural Networks written by James E. Smith and published by Morgan & Claypool Publishers. This book was released on 2017-05-18 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable. This book, written from the perspective of a computer designer and targeted at computer researchers, is intended to give both background and lay out a course of action for studying the brain's computational paradigm. It contains a mix of concepts and ideas drawn from computational neuroscience, combined with those of the author. As background, relevant biological features are described in terms of their computational and communication properties. The brain's neocortex is constructed of massively interconnected neurons that compute and communicate via voltage spikes, and a strong argument can be made that precise spike timing is an essential element of the paradigm. Drawing from the biological features, a mathematics-based computational paradigm is constructed. The key feature is spiking neurons that perform communication and processing in space-time, with emphasis on time. In these paradigms, time is used as a freely available resource for both communication and computation. Neuron models are first discussed in general, and one is chosen for detailed development. Using the model, single-neuron computation is first explored. Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Individual neurons are building blocks for constructing larger ensembles, referred to as "columns". These columns are trained in an unsupervised manner and operate collectively to perform the basic cognitive function of pattern clustering. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters. Larger cognitive systems are formed by combining columns into a hierarchical architecture. These higher level architectures are the subject of ongoing study, and progress to date is described in detail in later chapters. Simulation plays a major role in model development, and the simulation infrastructure developed by the author is described.

Download or read book Time Space Spiking Neural Networks and Brain Inspired Artificial Intelligence written by Nikola K. Kasabov and published by Springer. This book was released on 2018-08-29 with total page 742 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Download or read book Artificial Intelligence in the Age of Neural Networks and Brain Computing written by Robert Kozma and published by Academic Press. This book was released on 2023-10-11 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

Download or read book Robotic Computing on FPGAs written by Shaoshan Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the state-of-the-art field-programmable gate array (FPGA)-based robotic computing accelerator designs and summarizes their adopted optimized techniques. This book consists of ten chapters, delving into the details of how FPGAs have been utilized in robotic perception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed descriptions of how FPGAs have been used in robotic products, including commercial autonomous vehicles and space exploration robots.

Download or read book In Near Memory Computing written by Daichi Fujiki and published by Springer Nature. This book was released on 2022-05-31 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured introduction of the key concepts and techniques that enable in-/near-memory computing. For decades, processing-in-memory or near-memory computing has been attracting growing interest due to its potential to break the memory wall. Near-memory computing moves compute logic near the memory, and thereby reduces data movement. Recent work has also shown that certain memories can morph themselves into compute units by exploiting the physical properties of the memory cells, enabling in-situ computing in the memory array. While in- and near-memory computing can circumvent overheads related to data movement, it comes at the cost of restricted flexibility of data representation and computation, design challenges of compute capable memories, and difficulty in system and software integration. Therefore, wide deployment of in-/near-memory computing cannot be accomplished without techniques that enable efficient mapping of data-intensive applications to such devices, without sacrificing accuracy or increasing hardware costs excessively. This book describes various memory substrates amenable to in- and near-memory computing, architectural approaches for designing efficient and reliable computing devices, and opportunities for in-/near-memory acceleration of different classes of applications.

Download or read book AI for Computer Architecture written by Lizhong Chen and published by Springer Nature. This book was released on 2022-05-31 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated computer architecture designs.

Download or read book Quantum Computer Systems written by Yongshan Ding and published by Springer Nature. This book was released on 2022-05-31 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book targets computer scientists and engineers who are familiar with concepts in classical computer systems but are curious to learn the general architecture of quantum computing systems. It gives a concise presentation of this new paradigm of computing from a computer systems' point of view without assuming any background in quantum mechanics. As such, it is divided into two parts. The first part of the book provides a gentle overview on the fundamental principles of the quantum theory and their implications for computing. The second part is devoted to state-of-the-art research in designing practical quantum programs, building a scalable software systems stack, and controlling quantum hardware components. Most chapters end with a summary and an outlook for future directions. This book celebrates the remarkable progress that scientists across disciplines have made in the past decades and reveals what roles computer scientists and engineers can play to enable practical-scale quantum computing.

Download or read book On Chip Networks written by Natalie Enright Jerger and published by Morgan & Claypool Publishers. This book was released on 2017-06-19 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book targets engineers and researchers familiar with basic computer architecture concepts who are interested in learning about on-chip networks. This work is designed to be a short synthesis of the most critical concepts in on-chip network design. It is a resource for both understanding on-chip network basics and for providing an overview of state of the-art research in on-chip networks. We believe that an overview that teaches both fundamental concepts and highlights state-of-the-art designs will be of great value to both graduate students and industry engineers. While not an exhaustive text, we hope to illuminate fundamental concepts for the reader as well as identify trends and gaps in on-chip network research. With the rapid advances in this field, we felt it was timely to update and review the state of the art in this second edition. We introduce two new chapters at the end of the book. We have updated the latest research of the past years throughout the book and also expanded our coverage of fundamental concepts to include several research ideas that have now made their way into products and, in our opinion, should be textbook concepts that all on-chip network practitioners should know. For example, these fundamental concepts include message passing, multicast routing, and bubble flow control schemes.

Download or read book On Chip Networks Second Edition written by Natalie Enright Jerger and published by Springer Nature. This book was released on 2022-05-31 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book targets engineers and researchers familiar with basic computer architecture concepts who are interested in learning about on-chip networks. This work is designed to be a short synthesis of the most critical concepts in on-chip network design. It is a resource for both understanding on-chip network basics and for providing an overview of state of-the-art research in on-chip networks. We believe that an overview that teaches both fundamental concepts and highlights state-of-the-art designs will be of great value to both graduate students and industry engineers. While not an exhaustive text, we hope to illuminate fundamental concepts for the reader as well as identify trends and gaps in on-chip network research. With the rapid advances in this field, we felt it was timely to update and review the state of the art in this second edition. We introduce two new chapters at the end of the book. We have updated the latest research of the past years throughout the book and also expanded our coverage of fundamental concepts to include several research ideas that have now made their way into products and, in our opinion, should be textbook concepts that all on-chip network practitioners should know. For example, these fundamental concepts include message passing, multicast routing, and bubble flow control schemes.

Download or read book The Datacenter as a Computer written by Luiz André Barroso and published by Springer Nature. This book was released on 2022-06-01 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes warehouse-scale computers (WSCs), the computing platforms that power cloud computing and all the great web services we use every day. It discusses how these new systems treat the datacenter itself as one massive computer designed at warehouse scale, with hardware and software working in concert to deliver good levels of internet service performance. The book details the architecture of WSCs and covers the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. Each chapter contains multiple real-world examples, including detailed case studies and previously unpublished details of the infrastructure used to power Google's online services. Targeted at the architects and programmers of today's WSCs, this book provides a great foundation for those looking to innovate in this fascinating and important area, but the material will also be broadly interesting to those who just want to understand the infrastructure powering the internet. The third edition reflects four years of advancements since the previous edition and nearly doubles the number of pictures and figures. New topics range from additional workloads like video streaming, machine learning, and public cloud to specialized silicon accelerators, storage and network building blocks, and a revised discussion of data center power and cooling, and uptime. Further discussions of emerging trends and opportunities ensure that this revised edition will remain an essential resource for educators and professionals working on the next generation of WSCs.

Download or read book Proceedings of Seventh International Congress on Information and Communication Technology written by Xin-She Yang and published by Springer Nature. This book was released on 2022-07-26 with total page 889 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-quality research papers presented at the Seventh International Congress on Information and Communication Technology, held at Brunel University, London, on February 21–24, 2022. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT) and e-mining. Written by respected experts and researchers working on ICT, the book offers a valuable asset for young researchers involved in advanced studies. The work is presented in four volumes.

Download or read book Architectural and Operating System Support for Virtual Memory written by Abhishek Bhattacharjee and published by Springer Nature. This book was released on 2022-05-31 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides computer engineers, academic researchers, new graduate students, and seasoned practitioners an end-to-end overview of virtual memory. We begin with a recap of foundational concepts and discuss not only state-of-the-art virtual memory hardware and software support available today, but also emerging research trends in this space. The span of topics covers processor microarchitecture, memory systems, operating system design, and memory allocation. We show how efficient virtual memory implementations hinge on careful hardware and software cooperation, and we discuss new research directions aimed at addressing emerging problems in this space. Virtual memory is a classic computer science abstraction and one of the pillars of the computing revolution. It has long enabled hardware flexibility, software portability, and overall better security, to name just a few of its powerful benefits. Nearly all user-level programs today take for granted that they will have been freed from the burden of physical memory management by the hardware, the operating system, device drivers, and system libraries. However, despite its ubiquity in systems ranging from warehouse-scale datacenters to embedded Internet of Things (IoT) devices, the overheads of virtual memory are becoming a critical performance bottleneck today. Virtual memory architectures designed for individual CPUs or even individual cores are in many cases struggling to scale up and scale out to today's systems which now increasingly include exotic hardware accelerators (such as GPUs, FPGAs, or DSPs) and emerging memory technologies (such as non-volatile memory), and which run increasingly intensive workloads (such as virtualized and/or "big data" applications). As such, many of the fundamental abstractions and implementation approaches for virtual memory are being augmented, extended, or entirely rebuilt in order to ensure that virtual memory remains viable and performant in the years to come.

Download or read book Compiling Algorithms for Heterogeneous Systems written by Steven Bell and published by Springer Nature. This book was released on 2022-05-31 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most emerging applications in imaging and machine learning must perform immense amounts of computation while holding to strict limits on energy and power. To meet these goals, architects are building increasingly specialized compute engines tailored for these specific tasks. The resulting computer systems are heterogeneous, containing multiple processing cores with wildly different execution models. Unfortunately, the cost of producing this specialized hardware—and the software to control it—is astronomical. Moreover, the task of porting algorithms to these heterogeneous machines typically requires that the algorithm be partitioned across the machine and rewritten for each specific architecture, which is time consuming and prone to error. Over the last several years, the authors have approached this problem using domain-specific languages (DSLs): high-level programming languages customized for specific domains, such as database manipulation, machine learning, or image processing. By giving up generality, these languages are able to provide high-level abstractions to the developer while producing high-performance output. The purpose of this book is to spur the adoption and the creation of domain-specific languages, especially for the task of creating hardware designs. In the first chapter, a short historical journey explains the forces driving computer architecture today. Chapter 2 describes the various methods for producing designs for accelerators, outlining the push for more abstraction and the tools that enable designers to work at a higher conceptual level. From there, Chapter 3 provides a brief introduction to image processing algorithms and hardware design patterns for implementing them. Chapters 4 and 5 describe and compare Darkroom and Halide, two domain-specific languages created for image processing that produce high-performance designs for both FPGAs and CPUs from the same source code, enabling rapid design cycles and quick porting of algorithms. The final section describes how the DSL approach also simplifies the problem of interfacing between application code and the accelerator by generating the driver stack in addition to the accelerator configuration. This book should serve as a useful introduction to domain-specialized computing for computer architecture students and as a primer on domain-specific languages and image processing hardware for those with more experience in the field.

Download or read book Innovations in the Memory System written by Rajeev Balasubramonian and published by Springer Nature. This book was released on 2022-05-31 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: The memory system has the potential to be a hub for future innovation. While conventional memory systems focused primarily on high density, other memory system metrics like energy, security, and reliability are grabbing modern research headlines. With processor performance stagnating, it is also time to consider new programming models that move some application computations into the memory system. This, in turn, will lead to feature-rich memory systems with new interfaces. The past decade has seen a number of memory system innovations that point to this future where the memory system will be much more than dense rows of unintelligent bits. This book takes a tour through recent and prominent research works, touching upon new DRAM chip designs and technologies, near data processing approaches, new memory channel architectures, techniques to tolerate the overheads of refresh and fault tolerance, security attacks and mitigations, and memory scheduling.