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.