Algorithmic classes for acceleration via Hardware Sample Clauses

Algorithmic classes for acceleration via Hardware. Following extensive collaboration with all partners and especially those involved in WP2, several classes of algorithms were identified and studied. These algorithms are Support Vector Machines (SVM), Latent Dirichlet Analysis (LDA), Count Min, Exponential Histogram, and Xxxxxxx-Xxxxxxx Correlation Estimator. All these classes of algorithms were methodically evaluated. All of these algorithms were chosen by the software partners and studied by the hardware partners of the QualiMaster project, so that the results would be relevant to the project goals.An important contribution of the QualiMaster project is that through interaction of the software and hardware design teams of the project, the algorithms that are chosen by the software team form a real-life area in which the hardware team needs to apply its expertise, and likewise, the capabilities of the specialized reconfigurable hardware allow for the software team to have an alternative computing paradigm, which would not be possible to consider otherwise.
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