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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.
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 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.
"As soon as she heard me enter, Elvia awoke from a light sleep that had overcome her as she anxiously waited: 'How did it go?' Excited, I exclaimed: 'It works!' We embraced, almost overwhelmed with feelings of euphoria and happiness, aware that something epochal had happened. On that cold January night of 1971, the world's first microprocessor was born!" The creation of the microprocessor launched the digital age. The key technology allowing unprecedented integration, and the design of the world's first microprocessor, the Intel 4004, were the achievement of Federico Faggin. Shrinking an entire computer onto a tiny and inexpensive piece of silicon would come to define our daily lives, imbuing myriad devices and everyday objects with computational intelligence. In Silicon, internationally recognized inventor and entrepreneur Federico Faggin chronicles his "four lives" his formative years in war-torn Northern Italy; his pioneering work in American microelectronics; his successful career as a high-tech entrepreneur; and his more recent explorations into the mysteries of consciousness. In this heartfelt memoir, Faggin paints vivid anecdotes, steps readers through society-changing technological breakthroughs, and shares personal insights, as each of his lives propels the next.
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.
Electronic communication is radically altering literacy practices. Silicon Literacies unravels the key features of the new communication order to explore the social, cultural and educational impact of silicon literacy practices. Written by leading international scholars from a range of disciplines, the essays in this collection examine the implications of text produced on a keyboard, visible on a screen and transmitted through a global network of computers. The book covers topics as diverse as role-playing in computer games, the use of graphic symbols in on-screen texts and Internet degree programmes to reveal that being literate is to do with understanding how different modalities combine to create meaning. Recognizing that reading and writing are only part of what people have to learn to be literate, the contributors enhance our understanding of the ways in which the use of new technologies influence, shape and sometimes transform literacy practices.
"Universities and colleges often operate between two worlds: higher education and economic systems. It is impossible to understand how current developments are affecting colleges without attending to the changes in both the higher education system and in the economic communities in which they exist. W. Richard Scott, Michael W. Kirst, and colleagues focus on the changing relations between colleges and companies in one vibrant economic region: the San Francisco Bay Area. Colleges and tech companies, they argue, have a common interest in knowledge generation and human capital, but they operate in social worlds that substantially differ, making them uneasy partners. Colleges are a part of a long tradition that stresses the importance of precedent, academic values, and liberal education. High-tech companies, by contrast, value innovation and know-how, and they operate under conditions that reward rapid response to changing opportunities. The economy is changing faster than the postsecondary education system."--The cover.
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
It focuses on the ways in which various types of colleges have endeavored—and often failed—to meet the demands of a vibrant economy and concludes with a discussion of current policy recommendations, suggestions for improvements and reforms at the state level, and a proposal to develop a regional body to better align educational and economic development.