Accelerating Time Series Analysis via Processing using Non-Volatile Memories
arxiv(2022)
摘要
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.
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关键词
time series analysis,time series,memories,processing,non-volatile
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