Automatic Hardware Implementation Tool for a Discrete Adaboost-Based Decision Algorithm

We propose a method and a tool for automatic generation of hardware implementation of a decision rule based on the Adaboost algorithm. We review the principles of the classification method and we evaluate its hardware implementation cost in terms of FPGA's slice, using different weak classifiers based on the general concept of hyperrectangle. The main novelty of our approach is that the tool allows the user to find automatically an appropriate tradeoff between classification performances and hardware implementation cost, and that the generated architecture is optimized for each training process. We present results obtained using Gaussian distributions and examples from UCI databases. Finally, we present an example of industrial application of real-time textured image segmentation.

[1]  Steven Salzberg,et al.  A Nearest Hyperrectangle Learning Method , 1991, Machine Learning.

[2]  Thomas G. Dietterich,et al.  An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms , 1995, Machine Learning.

[3]  Ian Page.  Constructing hardware-software systems from a single description , 1996, J. VLSI Signal Process..

[4]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[5]  Fan Yang,et al.  Access control: adaptation and real-time implantation of a face recognition method , 2001 .

[6]  Jiri Matas,et al.  Support vector machines for face authentication , 2002, Image Vis. Comput..

[7]  Lluís Màrquez i Villodre,et al.  Boosting Applied toe Word Sense Disambiguation , 2000, ECML.

[8]  Prithviraj Banerjee,et al.  Automatic translation of software binaries onto FPGAs , 2004, Proceedings. 41st Design Automation Conference, 2004..

[9]  Miteran,et al.  4 - Classification géométrique par polytopes de contraintes. Performances et intégration , 1994 .

[10]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[11]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  Lluís Màrquez i Villodre,et al.  Boosting Applied to Word Sense Disambiguation , 2000, ArXiv.

[14]  Y. Taright,et al.  FPGA implementation of a multilayer perceptron neural network using VHDL , 1998, ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344).

[15]  Giovanni De Micheli,et al.  Synthesis and Optimization of Digital Circuits , 1994 .

[16]  Gunnar Rätsch,et al.  Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Paul A. Viola,et al.  Boosting Image Retrieval , 2004, International Journal of Computer Vision.

[19]  Robert E. Schapire,et al.  The Boosting Approach to Machine Learning An Overview , 2003 .

[20]  Eric Johnson,et al.  Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry , 2000, IEEE Trans. Neural Networks Learn. Syst..

[21]  Frederic Truchetet,et al.  Analysis of compatibility between lighting devices and descriptive features using Parzen's Kernel: application to flaw inspection by artifical vision , 2000 .

[22]  Dominique Lavenier,et al.  Evaluation of the streams-C C-to-FPGA compiler: an applications perspective , 2001, FPGA '01.

[23]  P. Gorria,et al.  Architectures for a real time classification processor , 1994, Proceedings of IEEE Custom Integrated Circuits Conference - CICC '94.

[24]  Patrick Gorria,et al.  Classification géométrique par polytopes de contraintes , 1991 .

[25]  Gerhard Tröster,et al.  High-Level Area and Performance Estimation of Hardware Building Blocks on FPGAs , 2000, FPL.

[26]  Scott Hauck,et al.  The roles of FPGAs in reprogrammable systems , 1998, Proc. IEEE.

[27]  Bernhard Schölkopf,et al.  Support Vector methods in learning and feature extraction , 1998 .

[28]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[29]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[30]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[31]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[32]  Demessie Girma,et al.  Artificial Neural Network Implementation on a Fine-Grained FPGA , 1994, FPL.