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- W763368408 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." @default.
- W763368408 created "2016-06-24" @default.
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- W763368408 date "1995-05-01" @default.
- W763368408 modified "2023-09-27" @default.
- W763368408 title "Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware" @default.
- W763368408 hasPublicationYear "1995" @default.
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