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Register Your Forests: Decision Tree Ensemble Optimization by Explicit CPU Register Allocation
Extensive evaluations of the proposed method are conducted in comparison to the basic realization of C code from the high-level machine learning model and succeeding compilation.
FLInt: Exploiting Floating Point Enabled Integer Arithmetic for Efficient Random Forest Inference
In this paper, we provide \mdname, a full precision floating point comparison for random forests, by only using integer and logic operations.
Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Finally, we explore the influence of a novel regularizer that optimizes with respect to this metric, with the aim of providing a configurable trade-off in accuracy and BET.
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