模拟电子神经网络

H. Graf
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引用次数: 8

摘要

对实现神经网络的模拟电路技术的兴趣并没有减少,正如最近来自大学和工业界的大量设计所表明的那样。一组电路是包含“多重累积”神经元的网络,具有很大的互联性。使用模拟电路技术的主要动机是,如果只需要中等精度的计算,则乘法累加操作可以紧凑地实现。其他类型的网络是更特定于算法的,为一个功能而硬连接,例如Kohonen网络,或实现视觉或听觉系统功能的神经形态设计。除了少数采用CCD技术的设计外,大多数神经网络都是采用标准的CMOS技术构建的。目前已经有一些模拟神经网络芯片上市,并且越来越多的应用报告出现。这是模拟神经网络发展的重要一步,因为现在它们的实用性正在“现实世界”的应用中受到考验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analog Electronic Neural Networks
The interest in analog circuit techniques for implementing neural nets is undiminished, as is indicated by a large number of recent designs, coming from universities as well as from industry. One group of circuits are networks containing the "multiply-accumulate" neurons with a large interconnectivity. The main motivation for using analog circuit techniques is the fact that the multiply-accumulate operation can be implemented compactly, if only a moderate precision of the computation is required. Other types of networks are more algorithm-specific, hard-wired for one function, for example Kohonen networks, or neuromorphic designs implementing functions found in the visual or the auditory system. Most neural nets are built with standard CMOS technology, except for a few designs in CCD technology. A few analog neural net chips are now commercially available, and more and more reports of applications are appearing. This is a significant step in the development of analog neural nets, as now their usefulness is being put to the test in "real-world" applications.
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