Analog Computing-in-Memory (ACIM) is an emerging architecture to perform efficient AI edge computing. However, current ACIM designs usually have unscalable topology and still heavily rely on manual efforts. These drawbacks limit the ACIM application scenarios and lead to an undesired time-to-market. This work proposes an end-to-end automated ACIM based on a synthesizable architecture (EasyACIM). With a given array size and customized cell library, EasyACIM can generate layouts for ACIMs with various design specifications end-to-end automatically. Leveraging the multi-objective genetic algorithm (MOGA)-based design space explorer, EasyACIM can obtain high-quality ACIM solutions based on the proposed synthesizable architecture, targeting versatile application scenarios. The ACIM solutions given by EasyACIM have a wide design space and competitive performance compared to the state-of-the-art (SOTA) ACIMs.

翻译：模拟存内计算（ACIM）是一种新兴的用于高效AI边缘计算的架构。然而，当前的ACIM设计通常具有不可扩展的拓扑结构，且仍严重依赖人工。这些缺陷限制了ACIM的应用场景，并导致不理想的产品上市时间。本文提出了一种基于可综合架构的端到端自动化ACIM（EasyACIM）。给定阵列尺寸和定制化单元库，EasyACIM能够端到端自动生成具有不同设计规格的ACIM布局。借助基于多目标遗传算法（MOGA）的设计空间探索器，EasyACIM能够基于所提出的可综合架构，针对多样化应用场景获得高质量的ACIM解决方案。与当前最先进的ACIM相比，EasyACIM提供的ACIM解决方案具有更宽广的设计空间和具有竞争力的性能。