A Compact 3-D VLSI Classifier Using Bagging Threshold Network Ensembles

Amine Bermak, Dominique Martinez

Research output: Contribution to journalArticle

41 Citations (Scopus)

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 (3-D) multiprecision very large-scale integration (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-μm CMOS technology and packaged using MCM-V micro-packaging technology. The 3-D chip implements up to 192 TLUs operating at a speed of up to 48GCPPS and implemented in a volume of (w × L × h) = (2 × 2 × 0.7) cm3. The 3-D 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 3-D chip for various precisions and ensemble sizes is demonstrated through an electronic nose application.

Original languageEnglish
Pages (from-to)1097-1109
Number of pages13
JournalIEEE Transactions on Neural Networks
Volume14
Issue number5
DOIs
Publication statusPublished - Sep 2003
Externally publishedYes

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Keywords

  • Bagging
  • Decision trees
  • Three-dimensional (3-D) packaging technology
  • Threshold networks
  • Very large-scale integration (VLSI)

ASJC Scopus subject areas

  • Software
  • Medicine(all)
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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