This book is loosely based on a Multidisciplinary University Research Initiative (MURI) project and a few supplemental projects sponsored by the Of?ce of Naval Research (ONR) during the time frame of 2004–2009. The initial technical scope and vision of the MURI project was formulated by Drs. Larry Cooper and Joel Davis, both program of?cers at ONR at the time. The unifying theme of this MURI project and its companionefforts is the concept of cellular nonlinear/neuralnetwork (CNN) technology and its various extensions and chip implementations, including nanoscale sensors and the broadening ?eld of cellular wave computing. In recent years, CNN-based vision system drew much attention from vision scientists to device technologists and computer architects. Due to its early - plementation in a two-dimensional (2D) topography, it found success in early vision technologyapplications, such as focal-plane arrays, locally adaptable sensor/ processor integration, resulting in extremely high frame rates of 10,000 frames per second. More recently it drew increasing attention from computer architects, due to its intrinsic local interconnect architecture and parallel processing paradigm. As a result, a few spin-off companies have already been successful in bringing cel- lar wave computing and CNN technology to the market. This book aims to capture some of the recent advances in the ?eld of CNN research and a few select areas of applications.

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In this book the emerging and converging architecture of morphic cellular wave computers based on the concept of Cellular Neural/Nonlinear Network (CNN) is introduced in a practical way. The authors include descriptions of hardware architectures, software algorithms, as well as a possible new CNN cell based on memristor. The first single chip cellular wave computer- a vision system on a chip (VSoC) is also discussed.

Cellular Nanoscale Sensory Wave Computing is a result of a Multidisciplinary University Research Initiative (MURI) project that has been funded by the Office of Naval Research and completed recently. The results manifest a new way of thinking about sensory computing, as well as it is one of the first successful attempts to bridge the gap between nanoscale (smaller than 100 nm) devices and CMOS integrated circuits with stored programmable algorithms and software on the system level.

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