A micropower learning vector quantizer for parallel analog-to-digital data compression

被引：0

作者：

Lubkin, J ^{[1
]}

Cauwenberghs, G ^{[1
]}

机构：

[1] Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA

来源：

ISCAS '98 - PROCEEDINGS OF THE 1998 INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-6 | 1998年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An analog VLSI architecture for learning vector quantization (LVQ), with on-chip adaptation and dynamic storage of the analog templates, is presented. The architecture extends to Fuzzy ART and Kohonen self-organizing maps through digital programming. The analog memory and adaptive element of the LVQ cell comprise 6 MOS transistors and one capacitor, and provide for robust self-refresh of the dynamic analog storage. Total cell size including distance and adaptive computations is 80 x 70 lambda in scalable MOSIS technology. Experimental results from a fabricated 16 x 16 cell prototype in 2 mu m CMOS are included.

引用

页码：B58 / B61

页数：4