@article{Talaska:167718,
title = {Current Mode Euclidean Distance Calculation Circuit for Kohonen's Neural Network Implemented in CMOS 0.18μm Technology},
author = {Talaska, Tomasz and Dlugosz, Rafal Tomasz},
journal = {Proceedings of the Canadian Conference on Electrical and Computer Engineering (CCECE)},
pages = {437-440},
year = {2007},
abstract = {In this paper we present analog current mode Euclidean distance calculation (EDC) block, which calculates the distance between two current vectors. The proposed circuit is an important part of the CMOS-implemented Kohonen’s neural network (KNN) designed for medical applications. The input data vector is compared with the weights’ vector in each neuron in proposed KNN. The neuron, whose weights are the closest to the input training vector becomes the winner and in the next step changes its weights. Proposed EDC block performs several operations such as: subtraction, squaring and summing of the current signals. The output current is the exact measure of the Euclidean distance. Proposed circuit dissipates power 15 μW from 1.5 V voltage supply, working with 20 MHz input data frequency. The signal frequency as well as the power dissipation may be scaled down to 1 MHz and 300 nA. Proposed EDC circuit, in CMOS 0.18 μm technology, occupies area about 500 μm2.},
url = {http://infoscience.epfl.ch/record/167718},
doi = {10.1109/CCECE.2007.115},
}