Neural Information Processing and VLSI, Softcover reprint of the original 1st ed. 1995 The Springer International Series in Engineering and Computer Science Series, Vol. 304

Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques.
Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation.
The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has been especially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.

Preface. Part I: Paradigms and Models. 1. Introduction. 2. Artificial Neural Network Algorithms. 3. Other Computational Intelligence Topics. 4. Biologically-Inspired Vision Processing. 5. Cellular Neural Networks. 6. Paralleled Hardware Annealing for Optimal Solutions. Part II: VLSI Design Technology. 7. Design Methodologies of VLSI Neural Networks. 8. Analog VLSI Building Blocks. 9. Digital VLSI Neuroprocessors. Part III: Applications and System Prototyping. 10. Back-Propagation Neural Networks. 11. Self-Organization Neural Networks. 12. Advanced Vision Chips and Systems. 13. Photonic Neural Networks. 14. Smart-Pixel, Cellular Neural Network, and Chaotic Chips. 15. Various Subsystem and System Construction Examples. 16. Selected Commercial Products from Industry. A: Spice CMOS Level-2, Level-4, and BSIM_Plus Model Files. B: Basic VLSI Building Blocks. C: Current-Mode Circuits for Piecewise-Linear Functions. D: Selected Software Listing. Subject Index.