Titlek-NN Text Classification using an FPGA-Based Sparse Matrix Vector Multiplication Accelerator
Publication TypeConference Papers
AuthorsK. Townsend, S. Sun, T. Johnson, O. Attia, P. Jones and J. Zambreno
Conference NameProceedings of the IEEE International Conference on Electro/Information Technology (EIT)
Date PublishedMay

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’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.

Paper attachments: