Memristor-based Computation-in-Memory (CIM) is one of the emerging architectures for next-generation Big Data problems. Its design requires a radically new synthesis flow as the memristor is a passive device that uses resistances to encode its logic values. This article proposes a synthesis flow for mapping parallel applications on memristor-based CIM architecture. First, it employs solution templates that contain scheduling, placement, and routing information to map multiple algorithms with similar data flow graphs to the memristor crossbar; this template is named skeleton. Complex algorithms that do not fit a single skeleton can be solved by nested skeletons. Therefore, this approach can be applied to a wide range of applications while using a limited number of skeletons only. Second, it further improves the design when spatial and temporal patterns exist in input data. To accelerate simulation of generated SystemC models, we integrate MPI in skeletons. The synthesis flow and its additional features are verified with multiple applications, and the results are compared against a multicore platform. These experiments demonstrate the feasibility and the potential of this approach.

中文翻译：

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用于内存中计算架构的基于骨架的综合流程

基于忆阻器的内存计算 (CIM) 是解决下一代大数据问题的新兴架构之一。其设计需要全新的综合流程，因为忆阻器是一种使用电阻对其逻辑值进行编码的无源器件。本文提出了一种在基于忆阻器的 CIM 架构上映射并行应用的综合流程。首先，它采用包含调度、布局和路由信息的解决方案模板将具有相似数据流图的多个算法映射到忆阻器交叉开关；这个模板被命名为骨架。不适合单个骨架的复杂算法可以通过嵌套骨架来解决。因此，这种方法可以应用于广泛的应用，同时仅使用有限数量的骨架。第二，当输入数据中存在空间和时间模式时，它进一步改进了设计。为了加速生成的 SystemC 模型的仿真，我们将 MPI 集成到骨架中。综合流程及其附加功能已通过多个应用程序进行验证，并将结果与​​多核平台进行比较。这些实验证明了这种方法的可行性和潜力。