VLSI实现的一个全并行随机神经网络

J. Quero, J. G. Ortega, C. Janer, L. Franquelo
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引用次数: 5

摘要

提出了多层神经网络的纯数字随机实现。作者使用一种允许添加大量突触连接的架构开发了这种实现,前提是神经元的传递函数是硬限制函数。设计参数即最大脉冲密度与操作精度的关系式被用作设计准则。由此产生的电路易于配置和扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VLSI implementation of a fully parallel stochastic neural network
Presents a purely digital stochastic implementation of multilayer neural networks. The authors have developed this implementation using an architecture that permits the addition of a very large number of synaptic connections, provided that the neuron's transfer function is the hard limiting function. The expression that relates the design parameter, that is, the maximum pulse density, with the accuracy of the operations has been used as the design criterion. The resulting circuit is easily configurable and expandable.<>
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