JPL Technical Report Server

Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

Show simple item record

dc.contributor.author Zee, Frank en_US
dc.date.accessioned 2004-10-03T03:39:39Z
dc.date.available 2004-10-03T03:39:39Z
dc.date.issued 1995-05 en_US
dc.identifier.clearanceno 95-0745 en_US
dc.identifier.uri http://hdl.handle.net/2014/30879
dc.description.abstract The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance. en_US
dc.format.extent 5211172 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject.other Artificial Neural Network en_US
dc.title Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search


Browse

My Account