Data movement between memory and processing units poses an energy barrier to Von-Neumann-based architectures. In-memory computing (IMC) eliminates this barrier. RRAM-based IMC has been explored for data-intensive applications, such as artificial neural networks and matrix-vector multiplications that are considered as “soft” tasks where performance is a more important factor than accuracy. In “hard” tasks such as partial differential equations (PDEs), accuracy is a determining factor. In this brief, we propose ReLOPE, a fully RRAM crossbar-based IMC to solve PDEs using the Runge–Kutta numerical method with 97% accuracy. ReLOPE expands the operating range of solution by exploiting shifters to shift input data and output data. ReLOPE range of operation and accuracy can be expanded by using fine-grained step sizes by programming other RRAMs on the BL. Compared to software-based PDE solvers, ReLOPE gains $31.4\times $ energy reduction at only 3% accuracy loss.

中文翻译：

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ReLOPE：基于电阻式RAM的线性一阶偏微分方程求解器

内存和处理单元之间的数据移动对基于Von-Neumann的体系结构构成了障碍。内存中计算（IMC）消除了这一障碍。基于RRAM的IMC已针对数据密集型应用进行了探索，例如人工神经网络和矩阵向量乘法被认为是“软”任务，其中性能比准确性更重要。在诸如偏微分方程（PDE）之类的“艰巨”任务中，准确性是决定因素。在本简介中，我们提出了ReLOPE，这是一种完全基于RRAM交叉开关的IMC，可以使用Runge-Kutta数值方法以97％的精度求解PDE。ReLOPE通过利用移位器来移位输入数据和输出数据，从而扩展了解决方案的操作范围。通过在BL上编程其他RRAM，可以使用细粒度步长来扩展ReLOPE的操作范围和精度。 $ 31.4 \次$ 能耗降低，精度损失仅为3％。