title: Neural networks in analog hardware--design and implementation issues.
abstract: This paper presents a brief review of some analog hardware implementations of neural networks. Several criteria for the classification of general neural networks implementations are discussed and a taxonomy induced by these criteria is presented. The paper also discusses some characteristics of analog implementations as well as some trade-offs and issues identified in the work reviewed. Parameters such as precision, chip area, power consumption, speed and noise susceptibility are discussed in the context of neural implementations. A unified review of various "VLSI friendly" algorithms is also presented. The paper concludes with some conclusions drawn from the analysis of the implementations presented.
authors.affiliation: Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.
authors.forename: S
authors.initials: S
authors.lastname: Draghici
mesh_terms: D001185:Artificial Intelligence; D003202:Computers, Analog; D016571:Neural Networks, Computer