A Survey of Resource Management for Processing-in-Memory and Near-Memory Processing Architectures
A Survey of Resource Management for Processing-in-Memory and Near-Memory Processing Architectures
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DOI：
10.3390/jlpea10040030
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Kamil Khan;S. Pasricha;R. Kim
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文献类型：
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作者：
Kamil Khan;S. Pasricha;R. Kim
由于新兴的深度学习和大数据应用所涉及的数据量，与数据移动相关的操作已迅速成为瓶颈。以数据为中心的计算（DCC），由内存处理（PIM）和近内存处理（NMP）范式实现，旨在通过将计算更接近数据来加速这些类型的应用程序。在过去的几年里，研究人员提出了各种存储器架构，使DCC系统，如逻辑层的3D堆叠存储器或基于电荷共享的位操作的动态随机存取存储器（DRAM）。然而，特定于应用的存储器访问模式、功率和热问题、存储器技术限制以及不一致的性能增益使DCC系统中的计算卸载复杂化。因此，设计用于计算卸载的智能资源管理技术对于利用这种新范式所提供的潜力至关重要。在这篇文章中，我们调查的主要趋势，在管理PIM和NMP为基础的DCC系统，并提供了一个审查景观的资源管理技术采用的系统设计人员为这样的系统。此外，我们还讨论了DCC管理未来的挑战和机遇。
Due to the amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become a bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory processing (NMP) paradigms, aims to accelerate these types of applications by moving the computation closer to the data. Over the past few years, researchers have proposed various memory architectures that enable DCC systems, such as logic layers in 3D-stacked memories or charge-sharing-based bitwise operations in dynamic random-access memory (DRAM). However, application-specific memory access patterns, power and thermal concerns, memory technology limitations, and inconsistent performance gains complicate the offloading of computation in DCC systems. Therefore, designing intelligent resource management techniques for computation offloading is vital for leveraging the potential offered by this new paradigm. In this article, we survey the major trends in managing PIM and NMP-based DCC systems and provide a review of the landscape of resource management techniques employed by system designers for such systems. Additionally, we discuss the future challenges and opportunities in DCC management.