
@conference {TowSun15A,
	title = {k-NN Text Classification using an FPGA-Based Sparse Matrix Vector Multiplication Accelerator},
	booktitle = {Proceedings of the IEEE International Conference on Electro/Information Technology (EIT)},
	year = {2015},
	month = {May},
	abstract = {Text classification is an important enabling technology for a wide range of applications such as Internet search, email filtering, network intrusion detection, and data mining electronic documents in general. The k Nearest Neighbors (k-NN) text classification algorithm is among the most accurate classification approaches, but is also among the most computationally expensive. In this paper, we propose accelerating k-NN using a novel reconfigurable hardware based architecture. More specifically, we accelerate a k-NN application{\textquoteright}s core with an FPGA-based sparse matrix vector multiplication coprocessor. On average our implementation shows a speed up factor of 15 over a naive single threaded CPU implementation of k-NN text classification for our datasets, and a speed up factor of 1.5 over a 32-threaded parallelized CPU implementation.},
	author = {Kevin Townsend and Song Sun and Tyler Johnson and Osama Attia and Phillip Jones and Joseph Zambreno}
}
