在数字FIR网络中使用GREMLIN

Proceedings of Fifth International Conference on Microelectronics for Neural Networks Pub Date : 1996-02-12 DOI:10.1109/MNNFS.1996.493813

M. Diepenhorst, W. Jansen, J. Nijhuis, M. Schreiner, L. Spaanenburg, A. Ypma

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引用次数: 2

摘要

时滞神经网络非常适合于预测目的。一个特殊的实现是有限脉冲响应神经网络。引入GREMLIN架构就是为了适应这样的网络。它可以通过微流水线在传统的连接串行结构上实现85 MCPS的性能，并且可以通过其逻辑增强存储器的特性轻松进行参数化设计。生物医学应用的典型设计可以以级联方式进行训练并随后进行映射。

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Using the GREMLIN for digital FIR networks

Time-delay neural networks are well-suited for prediction purposes. A particular implementation is the Finite Impulse Response neural net. The GREMLIN architecture is introduced to accommodate such networks. It can be micropipelined to achieve a 85 MCPS performance on a conventional connection-serial structure and allows from its Logic-Enhance Memory nature an easily parametrized design. A typical design for biomedical applications can be trained in a Cascade fashion and subsequently mapped.

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Proceedings of Fifth International Conference on Microelectronics for Neural Networks

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