Title :
A circuit model for the adaptive properties of neural networks
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Abstract :
A simple probabilistic model of a two-layer neural net is proposed to quantize the fault tolerance characteristics of the network from circuit design perspectives. While the model assumes very simple distribution functions and an elementary architecture, the results illuminate the design tradeoffs that must be considered, especially in VLSI implementations. The methods to quantize fault tolerance that are presented are applicable to more complicated probability distributions and architectures
Keywords :
VLSI; adaptive systems; neural nets; reliability; VLSI implementations; adaptive properties; circuit model; design tradeoffs; elementary architecture; fault tolerance characteristics; probabilistic model; two-layer neural net; Circuit faults; Costs; Fault tolerance; Fault tolerant systems; Integrated circuit interconnections; Logic; Multiprocessor interconnection networks; Neural networks; Probability distribution; Redundancy;
Conference_Titel :
Systems, Man and Cybernetics, 1989. Conference Proceedings., IEEE International Conference on
Conference_Location :
Cambridge, MA
DOI :
10.1109/ICSMC.1989.71425
