基于在线算法的多层感知器反向传播的可编程硬件实现

Proceedings of Fifth International Conference on Microelectronics for Neural Networks Pub Date : 1996-02-12 DOI:10.1109/MNNFS.1996.493788

B. Girau, Arnaud Tisserand

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引用次数: 22

摘要

描述了一个全神经网络学习的数字硬件实现。它在fpga上使用在线算法。我们的解决方案的模块化避免了常见硬件电路的开发问题。对反向传播算法所需计算的精确分析使我们能够最大化实现的并行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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On-line arithmetic-based reprogrammable hardware implementation of multilayer perceptron back-propagation

A digital hardware implementation of a whole neural network learning is described. It uses on-line arithmetic on FPGAs. The modularity of our solution avoids the development problems that occur with more usual hardware circuits. A precise analysis of the computations required by the back-propagation algorithm allows us to maximize the parallism of our implementation.

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Proceedings of Fifth International Conference on Microelectronics for Neural Networks

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