一种自适应VLSI神经网络芯片

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI:10.1109/ICNN.1994.374523

R. Zaman, D. Wunsch

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引用次数: 4

摘要

提出了一种使用多重数模转换器(MDACs)作为突触权值的自适应神经网络。该芯片利用数字处理来学习权重，但保留了模拟系统的并行异步行为，因为部分神经元功能是模拟的。该芯片采用6位精度的MDAC单元。采用了Hebbian学习，这对电子神经网络非常有吸引力，因为它只使用局部信息来适应权重。

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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An adaptive VLSI neural network chip

Presents an adaptive neural network, which uses multiplying-digital-to-analog converters (MDACs) as synaptic weights. The chip takes advantage of digital processing to learn weights, but retains the parallel asynchronous behavior of analog systems, since part of the neuron functions are analog. The authors use MDAC units of 6 bit accuracy for this chip. Hebbian learning is employed, which is very attractive for electronic neural networks since it only uses local information in adapting weights.<>

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Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)

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