Toward Efficient Implementation of Artificial Neural Networks in Systems on Chip

Download Toward Efficient Implementation of Artificial Neural Networks in Systems on Chip PDF Online Free

Author :
Publisher :
ISBN 13 :
Total Pages : 138 pages
Book Rating : 4.:/5 (255 download)

DOWNLOAD NOW!


Book Synopsis Toward Efficient Implementation of Artificial Neural Networks in Systems on Chip by : Marek Ponca

Download or read book Toward Efficient Implementation of Artificial Neural Networks in Systems on Chip written by Marek Ponca and published by . This book was released on 2006 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Der Mangel an effektiv auf einem Chip implementierten künstlichen neuronalen Netze fordert einen neuen Ansatz. Die Ausnutzung der physikalischen Effekte, die uns die zur Realisierung benutzten Halbleitertechnologien bieten, können zum Beispiel für die flächensparende Implementierung genutzt werden. Neuronale Netze als integrierter Bestandteil komplexer Systeme können nur dann eingebettet werden, wenn die Voraussetzungen für eine kompakte Realisierung erfüllt sind. Dazu zählt vor allem eine flächensparende Implementierung aller Komponenten.Diese Arbeit behandelt neue Ansätze zur Implementierung künstlicher neuronaler Netze in digitaler Hardware. Da diese meistens wesentlich mehr Chipfläche bedürfen, dafür aber mit wesentlich höherer Bandbreite und Datenraten arbeiten, wird es immer den Bedarf an platzoptimierten Realisierungen geben. Im weiteren wird eine Vorgehensweise für die Implemetierung temporärer Dynamik neuronaler Potentiale mit minimierter Ressourcen-Ausnutzung präsentiert. Als Beispielapplikation wurde ein System für die Schallquellenlokalisierung benutzt.Zusätzlich wurde eine Methode entwickelt, mit der es möglich ist, die Hardware-Realisierungen der künstlichen neuronalen Netze miteinander zu vergleichen und objektiv zu bewerten. Verschiedene Kriterien und Ansichtsweisen können dabei in Betracht gezogen werden, z.B. ob der Wert auf der Genauigkeit der Nachbildung liegt oder ob eher die Rechenleistung im Vordergrund steht.

Towards Efficient Implementation of Artifical Neural Networks in Systems on Chip

Download Towards Efficient Implementation of Artifical Neural Networks in Systems on Chip PDF Online Free

Author :
Publisher :
ISBN 13 : 9783938843239
Total Pages : 131 pages
Book Rating : 4.8/5 (432 download)

DOWNLOAD NOW!


Book Synopsis Towards Efficient Implementation of Artifical Neural Networks in Systems on Chip by : Marek Ponca

Download or read book Towards Efficient Implementation of Artifical Neural Networks in Systems on Chip written by Marek Ponca and published by . This book was released on 2007 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Der Mangel an effektiv auf einem Chip implementierten künstlichen neuronalen Netze fordert einen neuen Ansatz. Die Ausnutzung der physikalischen Effekte, die uns die zur Realisierung benutzten Halbleitertechnologien bieten, können zum Beispiel für die flächensparende Implementierung genutzt werden. Neuronale Netze als integrierter Bestandteil komplexer Systeme können nur dann eingebettet werden, wenn die Voraussetzungen für eine kompakte Realisierung erfüllt sind. Dazu zählt vor allem eine flächensparende Implementierung aller Komponenten.Diese Arbeit behandelt neue Ansätze zur Implementierung künstlicher neuronaler Netze in digitaler Hardware. Da diese meistens wesentlich mehr Chipfläche bedürfen, dafür aber mit wesentlich höherer Bandbreite und Datenraten arbeiten, wird es immer den Bedarf an platzoptimierten Realisierungen geben. Im weiteren wird eine Vorgehensweise für die Implemetierung temporärer Dynamik neuronaler Potentiale mit minimierter Ressourcen-Ausnutzung präsentiert. Als Beispielapplikation wurde ein System für die Schallquellenlokalisierung benutzt.Zusätzlich wurde eine Methode entwickelt, mit der es möglich ist, die Hardware-Realisierungen der künstlichen neuronalen Netze miteinander zu vergleichen und objektiv zu bewerten. Verschiedene Kriterien und Ansichtsweisen können dabei in Betracht gezogen werden, z.B. ob der Wert auf der Genauigkeit der Nachbildung liegt oder ob eher die Rechenleistung im Vordergrund steht.

Neuromorphic Computing Principles and Organization

Download Neuromorphic Computing Principles and Organization PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3030925250
Total Pages : 260 pages
Book Rating : 4.0/5 (39 download)

DOWNLOAD NOW!


Book Synopsis Neuromorphic Computing Principles and Organization by : Abderazek Ben Abdallah

Download or read book Neuromorphic Computing Principles and Organization written by Abderazek Ben Abdallah and published by Springer Nature. This book was released on 2022-05-31 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning

Download Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3031382307
Total Pages : 199 pages
Book Rating : 4.0/5 (313 download)

DOWNLOAD NOW!


Book Synopsis Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning by : Vikram Jain

Download or read book Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning written by Vikram Jain and published by Springer Nature. This book was released on 2023-09-15 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores and motivates the need for building homogeneous and heterogeneous multi-core systems for machine learning to enable flexibility and energy-efficiency. Coverage focuses on a key aspect of the challenges of (extreme-)edge-computing, i.e., design of energy-efficient and flexible hardware architectures, and hardware-software co-optimization strategies to enable early design space exploration of hardware architectures. The authors investigate possible design solutions for building single-core specialized hardware accelerators for machine learning and motivates the need for building homogeneous and heterogeneous multi-core systems to enable flexibility and energy-efficiency. The advantages of scaling to heterogeneous multi-core systems are shown through the implementation of multiple test chips and architectural optimizations.

High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture

Download High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 9819734770
Total Pages : 128 pages
Book Rating : 4.8/5 (197 download)

DOWNLOAD NOW!


Book Synopsis High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture by : Jinshan Yue

Download or read book High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture written by Jinshan Yue and published by Springer Nature. This book was released on with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:

FPGA Implementations of Neural Networks

Download FPGA Implementations of Neural Networks PDF Online Free

Author :
Publisher : Springer Science & Business Media
ISBN 13 : 0387284877
Total Pages : 365 pages
Book Rating : 4.3/5 (872 download)

DOWNLOAD NOW!


Book Synopsis FPGA Implementations of Neural Networks by : Amos R. Omondi

Download or read book FPGA Implementations of Neural Networks written by Amos R. Omondi and published by Springer Science & Business Media. This book was released on 2006-10-04 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.

VLSI for Neural Networks and Artificial Intelligence

Download VLSI for Neural Networks and Artificial Intelligence PDF Online Free

Author :
Publisher : Springer Science & Business Media
ISBN 13 : 1489913319
Total Pages : 318 pages
Book Rating : 4.4/5 (899 download)

DOWNLOAD NOW!


Book Synopsis VLSI for Neural Networks and Artificial Intelligence by : Jose G. Delgado-Frias

Download or read book VLSI for Neural Networks and Artificial Intelligence written by Jose G. Delgado-Frias and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.

Towards Efficient Implementation of Neuromorphic Systems with Emerging Device Technologies

Download Towards Efficient Implementation of Neuromorphic Systems with Emerging Device Technologies PDF Online Free

Author :
Publisher :
ISBN 13 : 9781339084589
Total Pages : 157 pages
Book Rating : 4.0/5 (845 download)

DOWNLOAD NOW!


Book Synopsis Towards Efficient Implementation of Neuromorphic Systems with Emerging Device Technologies by : Farnood Merrikh Bayat

Download or read book Towards Efficient Implementation of Neuromorphic Systems with Emerging Device Technologies written by Farnood Merrikh Bayat and published by . This book was released on 2015 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays with unbounded expansion of digital world, powerful information processing systems governed by deep learning algorithms are becoming more and more popular. In this situation, usage of fast, powerful, intelligent and trainable deep learning methods seems critical and unavoidable. However, despite of their inherent structural and conceptual differences, all of these intelligent methods and systems share one common property i.e. having enormous number of trainable parameters. However, from a hardware point of view, the size of a practical computing system is always determined based on available resources. In this dissertation, we study these deep learning methods from a hardware point of view and investigate the possibility of their hardware implementation based on two new emerging technologies i.e. resistive switching and floating gate (flash) devices. For this purpose, memristive devices are fabricated with high density in crossbar structure to create a network which then trained with modified RPROB rule to successfully classify images. In addition, biologically plausible spike-timing dependent plasticity rule and its dependence to initial state is demonstrated experimentally on these nano-scale devices. Similar procedure is followed for the other technology, i.e. flash devices. We modified and fabricated the conventional design of digital flash memories which provide us with the ability of individual programming of floating-gate transistors. Having large-scale neural networks in mind, an efficient and high speed tuning method is developed based on acquired dynamic and static models which are then tested experimentally on commercial devices. We have also experimentally investigated the possibility of implementing vector-to-matrix multiplier using these devices which is the main building block of most deep learning methods. Finally, a multi-layer neural network is designed and fabricated using this technology to classify handwritten digits.

Efficient Processing of Deep Neural Networks

Download Efficient Processing of Deep Neural Networks PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!


Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

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.

Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

Download Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3031399323
Total Pages : 481 pages
Book Rating : 4.0/5 (313 download)

DOWNLOAD NOW!


Book Synopsis Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing by : Sudeep Pasricha

Download or read book Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-10-09 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

Download Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 303119568X
Total Pages : 418 pages
Book Rating : 4.0/5 (311 download)

DOWNLOAD NOW!


Book Synopsis Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing by : Sudeep Pasricha

Download or read book Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-11-01 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.

A Simple FPGA-based Architecture Design of Reconfigurable Neural Network

Download A Simple FPGA-based Architecture Design of Reconfigurable Neural Network PDF Online Free

Author :
Publisher :
ISBN 13 :
Total Pages : 160 pages
Book Rating : 4.:/5 (878 download)

DOWNLOAD NOW!


Book Synopsis A Simple FPGA-based Architecture Design of Reconfigurable Neural Network by : Jaber Salem

Download or read book A Simple FPGA-based Architecture Design of Reconfigurable Neural Network written by Jaber Salem and published by . This book was released on 2013 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: In contrast with analog design, digital design and implementation of any logic circuit suffer much from the difficulty in terms of economy and implementation. Neural networks are artificial systems inspired by the brain's cognitive behavior, which can learn tasks with some degree of complexity, such as, optimization problems, text and speech recognition. Since the topology of neural networks is highly crucial to the performance, the reconfigurable ability of the neural network hardware is very essential. Reconfigurability factually means several different designs can be implemented on a single architecture. Therefore, this work proposes an efficient architecture to implement the reconfigurable back propagation and Hopfield neural networks. We specifically adopted the reconfigurable artificial neural networks approach to show how it is possible to build an efficient chip. Simple neural network models with an appropriate training were used to behave as traditional logic functions in the bit- level. In order to further reduce the hardware, memories-sharing method has been adopted. Also, a comparison between the proposed and traditional networks shows that the proposed network has significantly reduced the time delay and power consumption. Xilinx - ISE is used to synthesize our design. VHDL code is used to build the architecture. The architecture code is then downloaded to FPGAs (Field Programmable Gate Array) to implement the design. FPGAs are strong tools to implement ANNs as one can exploit concurrency and rapidly reconfigure to adapt the weights and topologies of an ANN. Also, XPower, as one of the best tools in Xilinx, was used to measure the total required power by our architecture. Finally, the results showed that the proposed reconfigurable architecture leads to a considerable decrease in the consumed power to almost 43% as well as the total time delay. Also, the architecture can easily be scalable as a future work and is able to cope with several network sizes with the same hardware.

Artificial Intelligence and Hardware Accelerators

Download Artificial Intelligence and Hardware Accelerators PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3031221702
Total Pages : 358 pages
Book Rating : 4.0/5 (312 download)

DOWNLOAD NOW!


Book Synopsis Artificial Intelligence and Hardware Accelerators by : Ashutosh Mishra

Download or read book Artificial Intelligence and Hardware Accelerators written by Ashutosh Mishra and published by Springer Nature. This book was released on 2023-03-15 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores new methods, architectures, tools, and algorithms for Artificial Intelligence Hardware Accelerators. The authors have structured the material to simplify readers’ journey toward understanding the aspects of designing hardware accelerators, complex AI algorithms, and their computational requirements, along with the multifaceted applications. Coverage focuses broadly on the hardware aspects of training, inference, mobile devices, and autonomous vehicles (AVs) based AI accelerators

Artificial Neural Networks - ICANN 2008

Download Artificial Neural Networks - ICANN 2008 PDF Online Free

Author :
Publisher : Springer Science & Business Media
ISBN 13 : 3540875581
Total Pages : 1012 pages
Book Rating : 4.5/5 (48 download)

DOWNLOAD NOW!


Book Synopsis Artificial Neural Networks - ICANN 2008 by : Věra Kůrková

Download or read book Artificial Neural Networks - ICANN 2008 written by Věra Kůrková and published by Springer Science & Business Media. This book was released on 2008 with total page 1012 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set LNCS 5163 and LNCS 5164 constitutes the refereed proceedings of the 18th International Conference on Artificial Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The second volume is devoted to pattern recognition and data analysis, hardware and embedded systems, computational neuroscience, connectionistic cognitive science, neuroinformatics and neural dynamics. it also contains papers from two special sessions coupling, synchronies, and firing patterns: from cognition to disease, and constructive neural networks and two workshops new trends in self-organization and optimization of artificial neural networks, and adaptive mechanisms of the perception-action cycle.

Learning Deep Learning

Download Learning Deep Learning PDF Online Free

Author :
Publisher : Addison-Wesley Professional
ISBN 13 : 0137470290
Total Pages : 1106 pages
Book Rating : 4.1/5 (374 download)

DOWNLOAD NOW!


Book Synopsis Learning Deep Learning by : Magnus Ekman

Download or read book Learning Deep Learning written by Magnus Ekman and published by Addison-Wesley Professional. This book was released on 2021-07-19 with total page 1106 pages. Available in PDF, EPUB and Kindle. Book excerpt: NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Benchmarking, Measuring, and Optimizing

Download Benchmarking, Measuring, and Optimizing PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3030495566
Total Pages : 371 pages
Book Rating : 4.0/5 (34 download)

DOWNLOAD NOW!


Book Synopsis Benchmarking, Measuring, and Optimizing by : Wanling Gao

Download or read book Benchmarking, Measuring, and Optimizing written by Wanling Gao and published by Springer Nature. This book was released on 2020-06-09 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019, held in Denver, CO, USA, in November 2019. The 20 full papers and 11 short papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections named: Best Paper Session; AI Challenges on Cambircon using AIBenc; AI Challenges on RISC-V using AIBench; AI Challenges on X86 using AIBench; AI Challenges on 3D Face Recognition using AIBench; Benchmark; AI and Edge; Big Data; Datacenter; Performance Analysis; Scientific Computing.

Spintronics-Based Neuromorphic Computing

Download Spintronics-Based Neuromorphic Computing PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 9819744458
Total Pages : 134 pages
Book Rating : 4.8/5 (197 download)

DOWNLOAD NOW!


Book Synopsis Spintronics-Based Neuromorphic Computing by : Debanjan Bhowmik

Download or read book Spintronics-Based Neuromorphic Computing written by Debanjan Bhowmik and published by Springer Nature. This book was released on with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: