A Compact 3D VLSI Classifier using Bagging Threshold Network Ensembles

Amine Bermak Dominique Martinez 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : A bagging ensemble consists of a set of classifiers trained independently and combined by a majority vote. Such a combination improves generalization performance but can require large amounts of memory and computation, a serious drawback for addressing portable real-time pattern recognition applications. We report here a compact three-dimensional multiprecision VLSI implementation of a bagging ensemble. In our circuit, individual classifiers are decision trees implemented as threshold networks -one layer of threshold logic units (TLUs) followed by combinatorial logic functions. The hardware was fabricated using $0.7 \mu m$ CMOS technology and packaged using MCM-V micro-packaging technology. The 3D chip implements up to 192 TLUs operating at a speed of up to 48GCPPS and implemented in a volume of $(w \times L \times h)=(2 \times 2 \times 0.7) cm^3$. The 3D circuit features a high level of programmability and flexibility offering the possibility to make an efficient use of the hardware resources in order to reduce the power consumption. Successful operation of the 3D chip for various precisions and ensemble sizes is demonstrated through an electronic nose application.
Type de document :
Article dans une revue
IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers, 2003, 14 (5), pp.1097- 1109
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https://hal.inria.fr/inria-00099628
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Soumis le : mardi 26 septembre 2006 - 09:39:33
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00099628, version 1

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Amine Bermak, Dominique Martinez. A Compact 3D VLSI Classifier using Bagging Threshold Network Ensembles. IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers, 2003, 14 (5), pp.1097- 1109. 〈inria-00099628〉

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