Analogue Imprecision in Mlp Training.

Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms.

This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implication for learning and network performance.

The aim of the book is to present a study of how including an imprecision model into a learning scheme as a "fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation.

In addition the study shows how such a scheme can give rise to significant performance enhancement.