Embedded Hardware Structures For Efficient Volume And In Field Diagnosis Of Random Logic Circuits

Embedded Hardware Structures for Efficient Volume and In-field Diagnosis of Random Logic Circuits

McGraw-Hill Concise Encyclopedia of Science and Technology, Sixth Edition

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A major revision of this classic encyclopedia covering all areas of science and technology, the McGraw-Hill Concise Encyclopedia of Science and Technology, Sixth Edition, is prepared for students, professionals, and general readers seeking concise yet authoritative overviews of topics in all major fields in science and technology. The McGraw-Hill Concise Encyclopedia of Science and Technology, Sixth Edition, satisfies the needs of readers for an authoritative, comprehensive reference work in a relatively compact format that provides the breadth of coverage of the McGraw-Hill Encyclopedia of Science & Technology, 10th Edition. Written in clear, nonspecialist language understandable to students and general readers, yet with sufficient depth for scientists, educators, and researchers, this definitive resource provides: 7100 concise articles covering disciplines of science and technology from acoustics to zoology Extensively revised content with new and rewritten articles Current and critical advances in fast-developing fields such as biomedical science, chemistry, computing and information technology, cosmology, environmental science, nanotechnology, telecommunications, and physics More than 1600 two-color illustrations 75 full-color plates Hundreds of tables and charts 1300 biographical sketches of famous scientists Index containing 30,000 entries Cross references to related articles Appendices including bibliographies and useful data McGraw-Hill Professional science reference products are supported by MHEST.com, a website offering updates to articles, periodic special features on important scientific topics, multimedia content, and other features enriching the reader's experience. We encourage readers to visit the site often. Fields Covered Include: Acoustics Aeronautics Agriculture Anthropology Archeology Astronomy Biochemistry Biology Chemistry Computers Cosmology Earth Science Engineering Environmental Science Forensic Science Forestry Genetics Geography Immunology Information Science Materials Science Mathematics Medicine and Pathology Meteorology and Climate Science Microbiology Nanotechnology Navigation Neuroscience Oceanography Paleontology Physics Physiology Psychiatry Psychology Telecommunications Theoretical Physics Thermodynamics Veterinary Medicine Virology Zoology

Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning, volume II

Towards the long-standing dream of artificial intelligence, two solution paths have been paved: (i) neuroscience-driven neuromorphic computing; (ii) computer science-driven machine learning. The former targets at harnessing neuroscience to obtain insights for brain-like processing, by studying the detailed implementation of neural dynamics, circuits, coding and learning. Although our understanding of how the brain works is still very limited, this bio-plausible way offers an appealing promise for future general intelligence. In contrast, the latter aims at solving practical tasks typically formulated as a cost function with high accuracy, by eschewing most neuroscience details in favor of brute force optimization and feeding a large volume of data. With the help of big data (e.g. ImageNet), high-performance processors (e.g. GPU, TPU), effective training algorithms (e.g. artificial neural networks with gradient descent training), and easy-to-use design tools (e.g. Pytorch, Tensorflow), machine learning has achieved superior performance in a broad spectrum of scenarios. Although acclaimed for the biological plausibility and the low power advantage (benefit from the spike signals and event-driven processing), there are ongoing debates and skepticisms about neuromorphic computing since it usually performs worse than machine learning in practical tasks especially in terms of the accuracy.