MDA: A Reconfigurable Memristor-Based Distance Accelerator for Time Series Mining on Data Centers

Abstract

The rapid development of Internet-of-Things is yielding a huge volume of time series data, the real-time mining of which becomes a major load for data centers. The computation bottleneck in time series data mining is distance function, which is the fundamental element of many high data mining tasks. Recently various software optimization and hardware acceleration techniques have been proposed to tackle the challenge. However, each of these techniques is only designed or optimized for a specific distance function. To address this problem, in this paper we propose MDA, a high-throughput reconfigurable memristor-based distance accelerator for real-time and energy-efficient data mining with time series in data centers. Common circuit structure is extracted for efficiency, and the circuit can be configured to any specific distance functions. Particularly, we adopt the emerging device memristor for the design of MDA. Comprehensive experiments are presented with public available datasets to evaluate the performance of the proposed MDA. Experimental results show that compared with existing works, MDA has achieved a speedup of $3.5\times $ – $376\times $ on performance and an improvement of 1–3 orders of magnitude on energy efficiency with little accuracy loss.