000167718 001__ 167718
000167718 005__ 20180317095002.0
000167718 0247_ $$2doi$$a10.1109/CCECE.2007.115  
000167718 037__ $$aCONF
000167718 245__ $$aCurrent Mode Euclidean Distance Calculation Circuit for Kohonen's Neural Network Implemented in CMOS 0.18μm Technology
000167718 269__ $$a2007
000167718 260__ $$c2007
000167718 336__ $$aConference Papers
000167718 520__ $$aIn 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. 
000167718 700__ $$aTalaska, Tomasz
000167718 700__ $$0243673$$aDlugosz, Rafal Tomasz$$g190071
000167718 7112_ $$aCanadian Conference on Electrical and Computer Engineering (CCECE)$$cVancouver, Canada$$dApril 22-26, 2007
000167718 773__ $$q437-440$$tProceedings of the Canadian Conference on Electrical and Computer Engineering (CCECE)
000167718 8564_ $$s365000$$uhttps://infoscience.epfl.ch/record/167718/files/TTa_2007.pdf$$yn/a$$zn/a
000167718 909CO $$ooai:infoscience.tind.io:167718$$pSTI$$pconf
000167718 909C0 $$0252263$$pESPLAB$$xU11964
000167718 917Z8 $$x190089
000167718 937__ $$aEPFL-CONF-167718
000167718 973__ $$aOTHER$$rREVIEWED$$sPUBLISHED
000167718 980__ $$aCONF