Autonomic Networking-on-Chip : Bio-Inspired Specification, Development, and Verification

Written for researchers and scientists, this book presents advanced reference material for readers who already have a basic understanding of NoC and are now ready to learn how to specify, develop, and verify ANoC using rigorous approaches. Exploring the theoretical knowledge required to achieve this, the text also presents formal and practical aspects in a straightforward way by detailing elemental components and briefly touching on the more advanced ones. The authors demonstrate how to use formal ANoC methods while making sound judgments and allowing for reasonable justifications.

Despite the growing mainstream importance and unique advantages of autonomic networking-on-chip (ANoC) technology, Autonomic Networking-On-Chip: Bio-Inspired Specification, Development, and Verification is among the first books to evaluate research results on formalizing this emerging NoC paradigm, which was inspired by the human nervous system.

The third book in the Embedded Multi-Core Systems series from CRC Press, this is an advanced technical guide and reference composed of contributions from prominent researchers in industry and academia around the world. A response to the critical need for a global information exchange and dialogue, it is written for engineers, scientists, practitioners, and other researchers who have a basic understanding of NoC and are now ready to learn how to specify, develop, and verify ANoC using rigorous approaches.

With illustrative figures to simplify contents and enhance understanding, this resource contains original, peer-reviewed chapters reporting on new developments and opportunities, emerging trends, and open research problems of interest to both the autonomic computing and network-on-chip communities. Coverage includes state-of-the-art ANoC architectures, protocols, technologies, and applications. This volume thoroughly explores the theory behind ANoC to illustrate strategies that enable readers to use formal ANoC methods yet still make sound judgments and allow for reasonable justifications in practice.