Boosting: From data to hardware using automatic implementation tool

We propose a method of automatic 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 term of FPGA's slice, using different weak classifiers based on the general concept of hyperrectangle. We show how to combine the weak classifiers in order to find an efficient trade-off between classification performances and hardware implementation cost. We present results obtained using examples coming from UCI databases.