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一种用于具有片上反向传播的人工神经网络的硬件高效可级联芯片组。

A hardware efficient cascadable chip set for ANN's with on-chip backpropagation.

作者信息

Lehmann T

机构信息

Computational Neural Network Center, Technical University of Denmark, Lyngby.

出版信息

Int J Neural Syst. 1993 Dec;4(4):351-8. doi: 10.1142/s0129065793000298.

Abstract

In this paper, an analogue, cascadable, CMOS chip set for artificial neural networks is presented. The chip set (a synapse chip and a neuron chip) offer on-chip back-propagation learning in a fully parallel, layered, feedforward network of arbitrary size and topology. The learning scheme is implemented with no extra circuits at the synapse sites (compared to the system without the learning scheme) and extra circuits of a complexity only about the same as the neurons at the neuron sites. Also, no additional wiring is required by the learning scheme. Measurements on an experimental chip set are presented.

摘要

本文介绍了一种用于人工神经网络的模拟、可级联的CMOS芯片组。该芯片组(一个突触芯片和一个神经元芯片)在任意大小和拓扑结构的全并行、分层、前馈网络中提供片上反向传播学习。与没有学习方案的系统相比,该学习方案在突触部位无需额外电路,在神经元部位只需复杂度与神经元相当的额外电路。此外,该学习方案无需额外布线。文中还给出了对实验芯片组的测量结果。

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