Training fixed-point classifiers for on-chip low-power implementation
In this article, we develop several novel algorithms to train classifiers that can be implemented on chip with low-power fixed-point arithmetic with extremely small word length. These algorithms are based on Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Logistic Regression (LR), and are referred to as LDA-FP, SVM-FP, and LR-FP, respectively. They incorporate the nonidealities (i.e., rounding and overflow) associated with fixed-point arithmetic into the offline training process so that the resulting classifiers are robust to these nonidealities. Mathematically, LDA-FP, SVM-FP, and LR-FP are formulated as mixed integer programming problems that can be robustly solved by the branch-and-bound methods described in this article. Our numerical experiments demonstrate that LDA-FP, SVM-FP, and LR-FP substantially outperform the conventional approaches for the emerging biomedical applications of brain decoding.
Albalawi, H; Li, Y; Li, X
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