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Fast and Accurate Training of Ensemble Models with FPGA-based Switch

Jiuxi Meng, Ce Guo, Nadeen Gebara and Wayne Luk

Imperial College London, UK


Random projection is gaining more attention in large scale machine learning. It has been proved to reduce the dimensionality of a set of data whilst approximately preserving the pairwise distance between points by multiplying the original dataset with a chosen matrix. However, projecting data to a lower dimension subspace typically reduces the training accuracy. In this paper, we propose a novel architecture that combines an FPGA-based switch with the ensemble learning method. This architecture enables reducing training time while maintaining high accuracy. Our initial result shows a speedup of 2.12-6.77 times using four different high dimensionality datasets.


[The authors opted for not publicly sharing a presentation video.]

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