论文标题
加速时间序列分析通过使用非易失性记忆进行处理
Accelerating Time Series Analysis via Processing using Non-Volatile Memories
论文作者
论文摘要
时间序列分析（TSA）是一个关键的工作量，可以从顺序数据集合中提取有价值的信息，例如检测心电图中的异常。子序列动态时间翘曲（SDTW）是高准确性TSA的最新算法。我们发现，SDTW在常规CPU和GPU平台上的性能和能源效率受到计算和内存单元之间数据移动的延迟和能源开销的负担。 SDTW在传统平台上表现出低算术强度和低数据重用，这是由于数据流动开销的不良摊销。为了提高SDTW算法的性能和能源效率，我们提出了MATSA，MATSA是TSA的第一个基于磁盘的RAM（MRAM）加速器。 MATSA基于MRAM横杆利用加工记忆（PUM），以最大程度地减少数据移动开销并利用SDTW中的并行性。 MATSA分别在服务器级CPU，GPU和Processing-Near-Near-Memory平台上提高了7.35x/6.15x/6.31x和能源效率。
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.
