Accelerating Time Series Analysis via Processing using Non-Volatile Memories

TL;DR

This paper introduces MATSA, an MRAM-based accelerator that significantly enhances the performance and energy efficiency of subsequence Dynamic Time Warping for time series analysis by reducing data movement overheads.

Contribution

The paper presents the first MRAM-based Processing-Using-Memory accelerator for TSA, achieving substantial improvements over traditional CPU, GPU, and PNM platforms.

Findings

Performance improved by up to 7.35x

Energy efficiency increased by up to 11.29x

Effective reduction of data movement overheads

Abstract

Time Series Analysis (TSA) is a critical workload to extract valuable information from collections of sequential data, e.g., detecting anomalies in electrocardiograms. Subsequence Dynamic Time Warping (sDTW) is the state-of-the-art algorithm for high-accuracy TSA. We find that the performance and energy efficiency of sDTW on conventional CPU and GPU platforms are heavily burdened by the latency and energy overheads of data movement between the compute and the memory units. sDTW exhibits low arithmetic intensity and low data reuse on conventional platforms, stemming from poor amortization of the data movement overheads. To improve the performance and energy efficiency of the sDTW algorithm, we propose MATSA, the first Magnetoresistive RAM (MRAM)-based Accelerator for TSA. MATSA leverages Processing-Using-Memory (PUM) based on MRAM crossbars to minimize data movement overheads and exploit parallelism in sDTW. MATSA improves performance by 7.35x/6.15x/6.31x and energy efficiency by 11.29x/4.21x/2.65x over server-class CPU, GPU, and Processing-Near-Memory platforms, respectively.