Computer-Aided Design of Machine Learning Algorithm: Training Fixed-Point Classifier for On-Chip Low-Power Implementation

In this paper, we propose a novel linear discriminant analysis algorithm, referred to as LDA-FP, to train on-chip classifiers that can be implemented with low-power fixed-point arithmetic with extremely small word length. LDA-FP incorporates the non-idealities (i.e., rounding and overflow) associated with fixed-point arithmetic into the training process so that the resulting classifiers are robust to these non-idealities. Mathematically, LDA-FP is formulated as a mixed integer programming problem that can be efficiently solved by a novel branch-and-bound method proposed in this paper. Our numerical experiments demonstrate that LDA-FP substantially outperforms the conventional approach for the emerging biomedical application of brain computer interface.