Titlebook: VLSI — Compatible Implementations for Artificial Neural Networks; Sied Mehdi Fakhraie,Kenneth Carless Smith Book 1997 Springer Science+Bus

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Design, Modeling, and Implementation of a Synapse-MOS Device,scuss general attributes of these two blocks. Then, we will discuss our goal of integrating them at a synaptic level into a single Synapse-MOS (SyMOS) device. Next we will describe our approach to implementation of a synaptic unit using a conventional double-poly CMOS technology. In this process, a

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Synapse-MOS Artificial Neural Networks (SANNs),s introduced previously. Then, we will provide a summary of the guidelines derived here for neural-network-hardware implementation. Next, the design and implementation of a family of synapse-MOS ANNs is described. After a review of training algorithms suitable for the training of our hardware, we wi

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Synapse-MOS Artificial Neural Networks (SANNs),nd implementation of a family of synapse-MOS ANNs is described. After a review of training algorithms suitable for the training of our hardware, we will demonstrate experimental results based on two fabricated chips. A discussion and summary of the results obtained concludes this chapter.

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Analog Quadratic Neural Networks (AQNNs),nd to have shorter training times and perform better in comparison to conventional networks having only linear synapses [83]. However, until now, their implementation in hardware has seemed too difficult for a real-time realization.

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0893-3405 hes to ANN implementations: analog, digital and pulse-coded. The analog approach is emphasized as the main one taken in the later chapters of the book. The area of VLSI implementation of ANNs has been progressing for the last 15 years, but not at the fast pace originally predicted. Several reasons h