前馈和递归神经网络的容错实现

1990 Proceedings. International Conference on Wafer Scale Integration Pub Date : 1990-01-23 DOI:10.1109/ICWSI.1990.63897

P. Franzon, D. van den Bout, J. Paulos, T. Miller, W. Snyder, T. Nagle, Wentai Liu

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引用次数: 2

摘要

许多用于实现晶圆级集成的容错技术也可用于构建灵活和可扩展的体系结构。这些技术应用于两种人工神经网络:具有反向传播的前馈模拟网络和有效的数字循环网络

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Defect tolerant implementations of feed-forward and recurrent neural networks

Many of the defect tolerant techniques employed to achieve wafer-scale integration can also be used to construct flexible and scalable architectures. These techniques are applied to two artificial neural networks: a feed-forward analog network with backpropagation and an efficient digital recurrent network.<>

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1990 Proceedings. International Conference on Wafer Scale Integration

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