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.
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.
Optics is entering all phases of computer technology. By providing new research and ideas, it brings the reader up to date on how and why optics is likely to be used in next generation computers and at the same time explains the unique advantage optics enjoys over conventional electronics and why this trend will continue. Covered are basic optical concepts such as mathematical derivations, optical devices for optical computing, optical associative memories, optical interconnections, and optical logic. Also suggested are a number of research activities that are reinforcing the trend toward optics in computing, including neural networks, the software crisis, highly parallel computation, progress in new semiconductors, the decreasing cost of laser diodes, communication industry investments in fiber optics, and advances in optical devices. Exercises, solutions sets, and examples are provided.
Computational complexity is one of the most beautiful fields of modern mathematics, and it is increasingly relevant to other sciences ranging from physics to biology. But this beauty is often buried underneath layers of unnecessary formalism, and exciting recent results like interactive proofs, phase transitions, and quantum computing are usually considered too advanced for the typical student. This book bridges these gaps by explaining the deep ideas of theoretical computer science in a clear and enjoyable fashion, making them accessible to non-computer scientists and to computer scientists who finally want to appreciate their field from a new point of view. The authors start with a lucid and playful explanation of the P vs. NP problem, explaining why it is so fundamental, and so hard to resolve. They then lead the reader through the complexity of mazes and games; optimization in theory and practice; randomized algorithms, interactive proofs, and pseudorandomness; Markov chains and phase transitions; and the outer reaches of quantum computing. At every turn, they use a minimum of formalism, providing explanations that are both deep and accessible. The book is intended for graduate and undergraduate students, scientists from other areas who have long wanted to understand this subject, and experts who want to fall in love with this field all over again.
Galileo Unbound traces the journey that brought us from Galileo's law of free fall to today's geneticists measuring evolutionary drift, entangled quantum particles moving among many worlds, and our lives as trajectories traversing a health space with thousands of dimensions. Remarkably, common themes persist that predict the evolution of species as readily as the orbits of planets or the collapse of stars into black holes. This book tells the history of spaces of expanding dimension and increasing abstraction and how they continue today to give new insight into the physics of complex systems. Galileo published the first modern law of motion, the Law of Fall, that was ideal and simple, laying the foundation upon which Newton built the first theory of dynamics. Early in the twentieth century, geometry became the cause of motion rather than the result when Einstein envisioned the fabric of space-time warped by mass and energy, forcing light rays to bend past the Sun. Possibly more radical was Feynman's dilemma of quantum particles taking all paths at once — setting the stage for the modern fields of quantum field theory and quantum computing. Yet as concepts of motion have evolved, one thing has remained constant, the need to track ever more complex changes and to capture their essence, to find patterns in the chaos as we try to predict and control our world.
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.
Real-Time Optical Information Processing covers the most recent developments in optical information processing, pattern recognition, neural computing, and materials for devices in optical computing. Intended for researchers and graduate students in signal and information processing with some elementary background in optics, the book provides both theoretical and practical information on the latest in information processing in all its aspects. Leading researchers in the field describe the significant signal processing algorithms architectures in optics as well as basic hardware concepts, such as the fundamentals of spatial light modulators. Each chapter begins with a review of basic concepts and follows with a discussion of recent advances in the field. A complete bibliography on the fundamentals of each topic is also included to aid the reader.Contributors are among the leading researchers in the areaChapters begin with reviews of basic conceptsComplete bibliographical information is included
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.
Optical Computing Hardware provides information pertinent to the advances in the development of optical computing hardware. This book discusses the two application areas, namely, high-performance computing and high-throughput photonic switching. Organized into 11 chapters, this book begins with an overview of the requirements on hardware from s system perspective. This text then presents the self-electro-optic-effect devices (SPEED), the vertical-cavity-surface- emitting microlasers (VCSEL), and the vertical-to-surface transmission electrophotonic device (VSTEP). Other chapters consider the fundamental principles of the devices and their operation either as logic devices or for optical interconnection applications. This book discusses as well the planar optical microlens as an example of a refractive microlens of the gradient-index type and explains the diffractive optical elements. The final chapter describes a method for writing and reading optically in parallel from a three-dimensional matrix by means of two-photon interaction in photochromic organic materials. This book is a valuable resource for engineers, scientists, and researchers.
