Accelerating Dynamic Time Warping With Memristor-Based Customized Fabrics

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 mining is the distance measure, in which dynamic time warping (DTW) is one of the most widely used distance measures. Recently, various software optimization and hardware acceleration techniques have been proposed for DTW acceleration. However, the throughput and energy efficiency of DTW are still big concerns considering the ever-increasing volume of times series. In this paper, we propose a high-throughput and efficient memristor-based DTW architecture for real-time time series mining on data centers. Specifically, memristors have been adopted for both computation and configuration of the computing architecture. The computation flow in this architecture is fully presented in a continuous and asynchronous manner. To improve the computation efficiency, we propose an early lower bound algorithm by exploiting the predictability in the circuit characteristic. Experiments are performed with module evaluation and end-to-end evaluation including three popular applications: 1) similarity search; 2) classification; and 3) anomaly detection. Experimental results indicate that, compared to existing approaches, the speedup and energy efficiency improvement are 12x-43x and 51x-287x, respectively.