The traditional computer architectures severely suffer from the bottleneck between the processing elements and memory that is the biggest barrier in front of their scalability. Nevertheless, the amount of data that applications need to process is increasing rapidly, especially after the era of big data and artificial intelligence. This fact forces new constraints in computer architecture design towards more data-centric principles. Therefore, new paradigms such as in-memory and near-memory processors have begun to emerge to counteract the memory bottleneck by bringing memory closer to computation or integrating them. Associative processors are a promising candidate for in-memory computation, which combines the processor and memory in the same location to alleviate the memory bottleneck. One of the applications that need iterative processing of a huge amount of data is stencil codes. Considering this feature, associative processors can provide a paramount advantage for stencil codes. For demonstration, two in-memory associative processor architectures for 2D stencil codes are proposed, implemented by both emerging memristor and traditional SRAM technologies. The proposed architecture achieves a promising efficiency for a variety of stencil applications and thus proves its applicability for scientific stencil computing.

中文翻译：

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通过内存中计算有效地加速模板应用程序。

传统的计算机体系结构严重受到处理元素和内存之间的瓶颈的困扰，这是其可扩展性面前的最大障碍。尽管如此，应用程序需要处理的数据量正在迅速增加，特别是在大数据和人工智能时代之后。这一事实迫使计算机体系结构设计面临更多以数据为中心的原则的新约束。因此，诸如内存中和近内存处理器之类的新范例已经开始出现，通过使内存更接近计算或集成它们来抵消内存瓶颈。关联处理器是内存计算的有前途的候选者，它将处理器和内存组合在同一位置以减轻内存瓶颈。模板代码是需要迭代处理大量数据的应用程序之一。考虑到此功能，关联处理器可以为模板代码提供最重要的优势。为了进行演示，提出了两种用于二维模板代码的内存关联处理器架构，这些架构由新兴的忆阻器和传统SRAM技术实现。所提出的体系结构为各种模板应用实现了令人鼓舞的效率，因此证明了其在科学模板计算中的适用性。