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首页> 外文会议>2019 56th ACM/IEEE Design Automation Conference >NAPEL: Near-Memory Computing Application Performance Prediction via Ensemble Learning
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NAPEL: Near-Memory Computing Application Performance Prediction via Ensemble Learning

机译：NAPEL：通过集成学习预测近内存计算应用程序的性能

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摘要

The cost of moving data between the memory/storage units and the compute units is a major contributor to the execution time and energy consumption of modern workloads in computing systems. A promising paradigm to alleviate this data movement bottleneck is near-memory computing (NMC), which consists of placing compute units close to the memory/storage units. There is substantial research effort that proposes NMC architectures and identifies work-loads that can benefit from NMC. System architects typically use simulation techniques to evaluate the performance and energy consumption of their designs. However, simulation is extremely slow, imposing long times for design space exploration. In order to enable fast early-stage design space exploration of NMC architectures, we need high-level performance and energy models.We present NAPEL, a high-level performance and energy estimation framework for NMC architectures. NAPEL leverages ensemble learning to develop a model that is based on micro architectural parameters and application characteristics. NAPEL training uses a statistical technique, called design of experiments, to collect representative training data efficiently. NAPEL provides early design space exploration 220× faster than a state-of-the-art NMC simulator, on average, with error rates of to 8.5% and 11.6% for performance and energy estimations, respectively, compared to the NMC simulator. NAPEL is also capable of making accurate predictions for previously-unseen applications.
机译：在内存/存储单元和计算单元之间移动数据的成本是导致计算系统中现代工作负载的执行时间和能耗的主要因素。缓解这种数据移动瓶颈的一种有希望的范例是近内存计算（NMC），它包括将计算单元放置在靠近内存/存储单元的位置。有大量研究工作提出了NMC架构，并确定了可从NMC中受益的工作量。系统设计师通常使用仿真技术来评估其设计的性能和能耗。但是，仿真非常缓慢，需要花费大量时间进行设计空间探索。为了能够快速进行NMC架构的早期设计空间探索，我们需要高级性能和能耗模型。我们提出了NAPEL，这是用于NMC架构的高级性能和能耗估算框架。 NAPEL利用集成学习来开发基于微体系结构参数和应用程序特征的模型。 NAPEL训练使用一种称为实验设计的统计技术来有效地收集代表性训练数据。与NMC模拟器相比，NAPEL提供的早期设计空间探索平均比最新的NMC模拟器快220倍，其性能和能量估计的错误率分别达到8.5％和11.6％。 NAPEL还能够为以前看不见的应用做出准确的预测。

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