VLSI artificial neural networks engineering

VLSI Artificial Neural Networks Engineering offers a unique engineering approach to the design of VLSI Artificial Neural Networks (ANNs). The design of analog, digital and mixed analog/digital VLSI ANNs are represented. A design methodology and a CAD environment are presented to highlight the tradeoff design factors. System applications of ANNs to automatic speech recognition and pattern recognition are included. Chapter 1 serves as an introduction. Chapters 2, 3, 4 and 5 deal with VLSI circuit design techniques (analog, digital and sampled data) and automated VLSI design environment for ANNs. Chapter 2 reports on a sampled data approach to the implementation of ANNs with application to character recognition. It also contains an overview of the different approaches of VLSI implementation of ANNs; explaining the advantage and disadvantage of each approach. In Chapter 3, the topic of design exploration of mixed analog/digital ANNs at the high level of the design hierarchy is addressed. The need for creating such a design automation environment, with its supporting CAD tools, is a necessary condition for the widespread use of application-specific chips of ANN implementation. In Chapter 4 the same topic of design exploration is discussed, but at the low level of the hierarchy and targeting analog implementation. Chapter 5 reports on all-digital implementation of ANNs using the Neocognitron as the ANN model. Chapters 6, 7, 8 and 9 deal with the application of ANNs to a number of fields. Chapter 6 addresses the topic of automatic speech recognition using neural predictive hidden Markov models. Chapter 7 deals with the topic of classification using minimum complexity ANNs. Chapter 8 addresses the topic of pattern recognition using a fuzzy clustering ANNs. Chapter 9 deals with speech recognition using pipelined ANNs. VLSI Artificial Neural Networks Engineering will be useful to researchers and graduated engineers working in the area of VLSI circuit and system design and to the students of upper-undergraduate and graduate level courses on analog circuits, digital circuits, ANNs and VLSI system applications.