Efficient training of neural gas vector quantizers with analog circuit implementation

Stefano Rovetta; Rodolfo Zunino

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Efficient training of neural gas vector quantizers with analog circuit implementation

机译：使用模拟电路实现高效训练神经气体矢量量化器

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This paper presents an algorithm for training vector quantizers with an improved version of the Neural Gas model, and its implementation in analog circuitry. Theoretical properties of the algorithm are proven that clarify the performance of themethod in terms of quantization quality, and motivate design aspects of the hardware implementation. The architecture for vector quantization training includes two chips, one for Euclidean distance computation, the other for programmable sorting ofcodevectors. Experimental results obtained in a real application (image coding) support both the algorithm's effectiveness and the hardware performance, which can speed up the training process by up to two orders of magnitude.

机译：本文提出了一种使用改进版本的神经气体模型训练向量量化器的算法，及其在模拟电路中的实现。该算法的理论特性得到了证明，阐明了该方法在量化质量方面的性能，并激发了硬件实现的设计方面。向量量化训练的架构包括两个芯片，一个用于欧几里得距离计算，另一个用于代码向量的可编程排序。在实际应用（图像编码）中获得的实验结果既支持算法的有效性，也支持硬件性能，可以将训练过程加快多达两个数量级。

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《IEEE Transactions on Automatic Control》 |1999年第6期|688-698|共11页
作者
Stefano Rovetta; Rodolfo Zunino;
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正文语种英语
中图分类自动化系统;
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Efficient training of neural gas vector quantizers with analog circuit implementation

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