Data-intensive applications are poised to benefit directly from processing-in-memory platforms, such as memristive Memory Processing Units, which allow leveraging data locality and performing stateful logic operations. Developing design automation flows for such platforms is a challenging and highly relevant research problem. In this work, we investigate the problem of minimizing delay under arbitrary area constraint for MAGIC-based in-memory computing platforms. We propose an end-to-end area constrained technology mapping framework, CONTRA. CONTRA uses Look-Up Table(LUT) based mapping of the input function on the crossbar array to maximize parallel operations and uses a novel search technique to move data optimally inside the array. CONTRA supports benchmarks in a variety of formats, along with crossbar dimensions as input to generate MAGIC instructions. CONTRA scales for large benchmarks, as demonstrated by our experiments. CONTRA allows mapping benchmarks to smaller crossbar dimensions than achieved by any other technique before, while allowing a wide variety of area-delay trade-offs. CONTRA improves the composite metric of area-delay product by 2.1x to 13.1x compared to seven existing technology mapping approaches.

中文翻译：

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CONTRA：用于忆阻存储器处理单元的区域约束技术映射框架

数据密集型应用程序准备直接受益于内存处理平台，例如忆阻内存处理单元，它允许利用数据局部性并执行有状态的逻辑操作。为此类平台开发设计自动化流程是一个具有挑战性且高度相关的研究问题。在这项工作中，我们研究了基于 MAGIC 的内存计算平台在任意区域约束下最小化延迟的问题。我们提出了一个端到端的区域受限技术映射框架 CONTRA。CONTRA 使用基于查找表 (LUT) 的交叉阵列上的输入函数映射来最大化并行操作，并使用新颖的搜索技术在阵列内优化移动数据。CONTRA 支持多种格式的基准测试，连同横杆尺寸作为输入以生成 MAGIC 指令。正如我们的实验所证明的那样，CONTRA 可用于大型基准测试。CONTRA 允许将基准映射到比以前任何其他技术实现的更小的交叉尺寸，同时允许进行各种面积延迟权衡。与现有的七种技术映射方法相比，CONTRA 将面积延迟乘积的综合度量提高了 2.1 倍至 13.1 倍。