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Author: Daniel Brunner Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110583496 Category : Science Languages : en Pages : 276
Book Description
Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.
Author: Daniel Brunner Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110583496 Category : Science Languages : en Pages : 276
Book Description
Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.
Author: Cornelia Denz Publisher: Springer Science & Business Media ISBN: 3663122727 Category : Computers Languages : en Pages : 467
Book Description
During the next years neural networks and systems amenable to instructions will extend their influence in science and technology. A prominent point of interest in this field is assigned to optical networks: they are small and flexible, and due to their ability of parallel processing they are devoted to the construction of small systems. This monograph explains the fundamentals of optical neural networks to physicists, engineers and device constructors.
Author: Kevin L. Priddy Publisher: SPIE Press ISBN: 9780819459879 Category : Computers Languages : en Pages : 184
Book Description
This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.
Author: Paul R. Prucnal Publisher: CRC Press ISBN: 1498725244 Category : Science Languages : en Pages : 412
Book Description
This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.
Author: Michael J. Connelly Publisher: Springer Science & Business Media ISBN: 0306481561 Category : Technology & Engineering Languages : en Pages : 172
Book Description
The semiconductor optical amplifier has emerged as an important component in many optical fibre communication, switching and signal processing systems. This invaluable information source provides a comprehensive and detailed treatment of the design and applications of SOAs.
Author: Pochi Yeh Publisher: World Scientific ISBN: 9789810214432 Category : Science Languages : en Pages : 628
Book Description
This book, intended for students, researchers and engineers, is a collection of classic papers on photorefractive nonlinear optics. Included are landmark papers on fundamental photorefractive phenomena, two-wave mixing, four-wave mixing, phase conjugators and resonators, material growth and physics, and applications in image processing, optical storage and optical computing.
Author: Katy Warr Publisher: "O'Reilly Media, Inc." ISBN: 1492044903 Category : Computers Languages : en Pages : 233
Book Description
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come
Author: Vivienne Sze Publisher: Springer Nature ISBN: 3031017668 Category : Technology & Engineering Languages : en Pages : 254
Book Description
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.
Author: Daniel Brunner
Author: Daniel Brunner
Author: Cornelia Denz
Author: Kevin L. Priddy
Author: Paul R. Prucnal
Author: Michael J. Connelly
Author: Pochi Yeh
Author: 
Author: Katy Warr
Author: Vivienne Sze
Author: Francis T. S. Yu