Scalable and Parameterized Architecture for Efficient Stream Mining

Dawei LI
Xuecheng ZOU
Xiaowei XU

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E101-A    No.1    pp.219-231
Publication Date: 2018/01/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E101.A.219
Type of Manuscript: PAPER
Category: Systems and Control
stream mining architecture,  parameterization,  dynamic time warping,  k-nearest neighbor,  

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With an annual growth of billions of sensor-based devices, it is an urgent need to do stream mining for the massive data streams produced by these devices. Cloud computing is a competitive choice for this, with powerful computational capabilities. However, it sacrifices real-time feature and energy efficiency. Application-specific integrated circuit (ASIC) is with high performance and efficiency, which is not cost-effective for diverse applications. The general-purpose microcontroller is of low performance. Therefore, it is a challenge to do stream mining on these low-cost devices with scalability and efficiency. In this paper, we introduce an FPGA-based scalable and parameterized architecture for stream mining.Particularly, Dynamic Time Warping (DTW) based k-Nearest Neighbor (kNN) is adopted in the architecture. Two processing element (PE) rings for DTW and kNN are designed to achieve parameterization and scalability with high performance. We implement the proposed architecture on an FPGA and perform a comprehensive performance evaluation. The experimental results indicate thatcompared to the multi-core CPU-based implementation, our approach demonstrates over one order of magnitude on speedup and three orders of magnitude on energy-efficiency.