Due to amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become the 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 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.

中文翻译：

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内存中处理和近内存处理架构的资源管理概览

由于新兴的深度学习和大数据应用涉及的数据量大，数据移动相关的操作迅速成为瓶颈。由内存处理 (PIM) 和近内存处理 (NMP) 范式支持的以数据为中心的计算 (DCC) 旨在通过将计算移至更接近数据的位置来加速这些类型的应用程序。在过去几年中，研究人员提出了支持 DCC 系统的各种存储器架构，例如 3D 堆叠存储器中的逻辑层或 DRAM 中基于电荷共享的按位运算。然而，特定于应用程序的内存访问模式、功率和热问题、内存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。所以，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提供的潜力至关重要。在本文中，我们调查了管理基于 PIM 和 NMP 的 DCC 系统的主要趋势，并回顾了系统设计人员为此类系统采用的资源管理技术的前景。此外，我们还讨论了 DCC 管理中的未来挑战和机遇。