Guest Editors’ Introduction: The Expanding World of Neural and Fuzzy Systems

UnivedyofPisa giving way to realistic expectations based on solid designs and a careful analysis of applications. A rapidly increasing number of industrial applications demonstrate that neural net hardware has moved beyond the laboratory stage and is proving itself in the real world. Nevertheless, many new concepts and ideas are still emerging, making it clear that this is far from a mature field. While a few years ago the goal of many researchers was the development of generalpurpose neural processors, today’s designs tend to be more specific to applications. Processors sufficiently general to satisfy the requirements of many different applications have proven ineffective. The most general processors, the digital neural signal processors, are basically multiple digital signal processors. Most researchers realize by now that it is not sufficient to present block diagrams of new types of synapses, neurons, or other components of a neural network and assume that everything else will fall into place. Designers must consider applications right from the beginning and cannot just design new synapses “in search of an application.” So, does this mean that a neural network is just another application-specific circuit? Not quitethe ambition of most designs is to cover not just one application, but a class of applications. This makes neural net designs particularly difficult, because commonalities among applications must be extracted and trade-offs between flexibility and performance have to be analyzed carefully. The systems and circuits we collected for this issue are good examples of this process. Many of the articles do not so much describe the details of a circuit as they focus on the influence of various trade-offs on the performance of a system. This special issue contains six articles selected from the presentations at Microneuro 94, the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, held last fall in Turin, Italy. Microneuro has emerged as the only international forum devoted exclusively to hardware implementations of neural and fuzzy systems. Though open to circuit and system implementations, it also welcomes applications of systems as well as theoretical developments of direct interest to hardware implementations. The program of the conference was full of interesting applications, ranging from pattern recognition and robotic vision over automatic control to time series forecasting. In all these areas, neural networks and fuzzy systems continue to gain more and more acceptance from the scientific and technological community. With the large selection of excellent contributions at the meeting, we found it difficult to make a choice that would fit into the space of this issue. We tried our best to cover a broad range of approaches, emphasizing systems where applications had been demonstrated. Today, fuzzy logic processors are more mature and more widely used than neural network hardware. Fuzzy logic engines form an integral part of many microcontrollers, and fuzzy logic supports countless control applications. The first article, by H. Eichfeld and coauthors, describes a large fuzzy processor. While the main application is in control, its large size also makes this processor of interest for pattern recognition applications. The chip computes up to 10 million rules/s, with up to 256 inputs, 64 outputs, and 16,384 rules. The article also introduces the basics of fuzzy computing and describes how these can be mapped on the proposed device. R. Coggins and coauthors describe an analog VLSI neural network for biomedical applications.