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Learning on Silicon by G. Cauwenberghs,Magdy Bayoumi Pdf
Learning on Silicon combines models of adaptive information processing in the brain with advances in microelectronics technology and circuit design. The premise is to construct integrated systems not only loaded with sufficient computational power to handle demanding signal processing tasks in sensory perception and pattern recognition, but also capable of operating autonomously and robustly in unpredictable environments through mechanisms of adaptation and learning. This edited volume covers the spectrum of Learning on Silicon in five parts: adaptive sensory systems, neuromorphic learning, learning architectures, learning dynamics, and learning systems. The 18 chapters are documented with examples of fabricated systems, experimental results from silicon, and integrated applications ranging from adaptive optics to biomedical instrumentation. As the first comprehensive treatment on the subject, Learning on Silicon serves as a reference for beginners and experienced researchers alike. It provides excellent material for an advanced course, and a source of inspiration for continued research towards building intelligent adaptive machines.
Artificial Neural Networks and Machine Learning -- ICANN 2012 by Alessandro Villa,Wlodzislaw Duch,Peter Erdi,Francesco Masulli,Günther Palm Pdf
The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.
Silicon Photonics for High-Performance Computing and Beyond by Mahdi Nikdast,Sudeep Pasricha,Gabriela Nicolescu,Ashkan Seyedi,Di Liang Pdf
Silicon photonics is beginning to play an important role in driving innovations in communication and computation for an increasing number of applications, from health care and biomedical sensors to autonomous driving, datacenter networking, and security. In recent years, there has been a significant amount of effort in industry and academia to innovate, design, develop, analyze, optimize, and fabricate systems employing silicon photonics, shaping the future of not only Datacom and telecom technology but also high-performance computing and emerging computing paradigms, such as optical computing and artificial intelligence. Different from existing books in this area, Silicon Photonics for High-Performance Computing and Beyond presents a comprehensive overview of the current state-of-the-art technology and research achievements in applying silicon photonics for communication and computation. It focuses on various design, development, and integration challenges, reviews the latest advances spanning materials, devices, circuits, systems, and applications. Technical topics discussed in the book include: • Requirements and the latest advances in high-performance computing systems • Device- and system-level challenges and latest improvements to deploy silicon photonics in computing systems • Novel design solutions and design automation techniques for silicon photonic integrated circuits • Novel materials, devices, and photonic integrated circuits on silicon • Emerging computing technologies and applications based on silicon photonics Silicon Photonics for High-Performance Computing and Beyond presents a compilation of 19 outstanding contributions from academic and industry pioneers in the field. The selected contributions present insightful discussions and innovative approaches to understand current and future bottlenecks in high-performance computing systems and traditional computing platforms, and the promise of silicon photonics to address those challenges. It is ideal for researchers and engineers working in the photonics, electrical, and computer engineering industries as well as academic researchers and graduate students (M.S. and Ph.D.) in computer science and engineering, electronic and electrical engineering, applied physics, photonics, and optics.
Embedded Deep Learning by Bert Moons,Daniel Bankman,Marian Verhelst Pdf
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
The Silicon Road to Chess Improvement by Matthew Sadler Pdf
‘How can I learn from AlphaZero’s games, aren’t they too advanced for me?’ many club players asked Matthew Sadler after reading his and Natasha Regan’s groundbreaking Game Changer. Here is the answer: you may not be able to replicate their dazzling deep calculations, but every chess player, from club level up, can improve their game by using engines. You will probably be surprised, there is so much more your engine can do for you than just checking and calculating variations! In this thought-provoking new book, based on many years of working with the world’s best chess software, Sadler presents a unique set of methods to work out using your engine. He shows how in your opening preparation, instead of sifting through masses of computer analysis you should play matches against your engine. He also explains how to train your early middlegame play, the conversion of advantages, your positional play, and your defence. And of course: how to analyse your own games. These generic training methods Sadler supplements with concrete middlegame and opening tools. He explains how the top engines tackle crucial middlegame themes such as entrenched pieces, whole board play, ‘attacking rhythm’, exchanging pieces, the march of the Rook’s pawn, queen versus pieces, and many others. He also opens your eyes to typical scenarios that the engines found and fine-tuned in popular openings such as the King’s Indian, the Grünfeld, the Slav, the French and the Sicilian. Sadler illustrates his lessons with a collection of fantastic games, explained with his trademark enthusiasm. For the first time the superhuman powers of the chess engine have been decoded to the benefit of all players, in a rich and highly instructive book.
The Boundaryless Career by Michael B. Arthur,Denise M. Rousseau Pdf
Organizational restructuring and global, hypercompetition have revolutionized careers and destroyed the traditional blueprint for advancement and career success. This book details the new forms work takes in the new organizational era where worker mobility has become critical to the well-being and learning of both people and firms. The Boundaryless Career approaches the new principle of the boundaryless career in five directions. The first section helps the reader explore the nature of boundaryless careers by highlighting some of their essential elements. The second section turns to competitive advantage and the role of workers' knowledge. The thirs section concentrates on the role of the social structure in the organizing of work. The fourth section turns to focus on how boundaryless careers affect personal development and growth. The fifth section addresses the demands boundaryless careers create for schools, communities, and other social institutions. Introductory and concluding chapters by the editors offer frameworks for conceptualizing careers now and in the future. The Boundaryless Career provides a conceptual map of new career and employment forms to the prospective benefit of people making career choices, companies re-crafting human resource practices, schools and universities re-considering their roles, and policy-makers concerned with regional or national competitiveness. It will be essential reading for scholars in a range of social science disciplines spanning themes of economics, management, education, organizational behavior, and the psychology and sociology of work. It will also appeal broadly to free thinkers interested in the changing nature of careers and employment as both people and firms tackle the realities of increasingly open markets and global competition.
Knowledge Management in the Learning Society by OECD Pdf
This book analyses and compares concretely the processes of knowledge production, dissemination and use in the engineering, the information and communication technology, the health and the education sectors.
Neuromorphic Systems Engineering by Tor Sverre Lande Pdf
Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include: large scale analog systems in silicon neuromorphic silicon auditory (ear) and vision (eye) systems in silicon learning and adaptation in silicon merging biology and technology micropower analog circuit design analog memory analog interchipcommunication on digital buses £/LIST£ Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
Post-Silicon Validation and Debug by Prabhat Mishra,Farimah Farahmandi Pdf
This book provides a comprehensive coverage of System-on-Chip (SoC) post-silicon validation and debug challenges and state-of-the-art solutions with contributions from SoC designers, academic researchers as well as SoC verification experts. The readers will get a clear understanding of the existing debug infrastructure and how they can be effectively utilized to verify and debug SoCs.
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.
Introduction to Silicon Valley Bank by Gilad James, PhD Pdf
Silicon Valley Bank (SVB) is a bank that specializes in providing financial services to technology and life science companies, as well as venture capital and private equity firms. The bank was founded in 1983 by a group of entrepreneurs in Santa Clara, California, who were frustrated with the traditional banking industry's lack of understanding and support for their high-growth, high-risk businesses. SVB has since grown to become one of the most prominent banks in the innovation economy, with offices across the United States and around the world. SVB's primary offerings include commercial banking, investment banking, and asset management services. The bank's commercial banking services include traditional banking products such as deposit accounts, loans, and lines of credit, as well as customized financial solutions for the unique needs of technology and life science companies. SVB's investment banking arm provides merger and acquisition advisory services, underwriting of public and private offerings, and strategic consulting to the bank's clients. Finally, the bank's asset management division manages investment funds that provide capital to venture capital and private equity firms, as well as direct investments in the bank's clients. Overall, SVB's focus on the innovation economy has allowed it to develop deep expertise in a niche market and build a compelling value proposition for technology and life science companies.
The discipline of neurodesign is a highly interdisciplinary one, while at the same time in the process of maturing towards real-life applications. The breakthrough about to be achieved is to close the loop in communication between neural systems and electronic and mechatronic systems and actually let the nervous system adapt to the feedback from the man-made systems. To master this loop, scientists need a sound understanding of neurology, from the cellular to the systems scale, of man-made systems and how to connect the two. These scientists comprise medical scientists, neurologists and physiologists, engineers, as well as biophysicists. And they need the topics in a coherently written work with chapters building upon another.
The Elgar Companion to Innovation and Knowledge Creation by Harald Bathelt,Patrick Cohendet,Sebastian Henn,Laurent Simon Pdf
This unique Companion provides a comprehensive overview and critical evaluation of existing conceptualizations and new developments in innovation research. It draws on multiple perspectives of innovation, knowledge and creativity from economics, geography, history, management, political science and sociology. The Companion brings together leading scholars to reflect upon innovation as a concept (Part I), innovation and institutions (Part II), innovation and creativity (Part III), innovation, networking and communities (Part IV), innovation in permanent spatial settings (Part V), innovation in temporary, virtual and open settings (Part VI), innovation, entrepreneurship and market making (Part VII), and the governance and management of innovation (Part VIII).
World War II and the West It Wrought by Mark Brilliant,David M. Kennedy Pdf
Few episodes in American history were more transformative than World War II, and in no region did it bring greater change than in the West. Having lifted the United States out of the Great Depression, World War II set in motion a massive westward population movement, ignited a quarter-century boom that redefined the West as the nation's most economically dynamic region, and triggered unprecedented public investment in manufacturing, education, scientific research, and infrastructure—an economic revolution that would lay the groundwork for prodigiously innovative high-tech centers in Silicon Valley, the Puget Sound area, and elsewhere. Amidst robust economic growth and widely shared prosperity in the post-war decades, Westerners made significant strides toward greater racial and gender equality, even as they struggled to manage the environmental consequences of their region's surging vitality. At the same time, wartime policies that facilitated the federal withdrawal of Western public lands and the occupation of Pacific islands for military use continued an ongoing project of U.S. expansionism at home and abroad. This volume explores the lasting consequences of a pivotal chapter in U.S. history, and offers new categories for understanding the post-war West. Contributors to this volume include Mark Brilliant, Geraldo L. Cadava, Matthew Dallek, Mary L. Dudziak, Jared Farmer, David M. Kennedy, Daniel J. Kevles, Rebecca Jo Plant, Gavin Wright, and Richard White.