LIM-GEN: A Data-guided Framework for Automated Generation of Heterogeneous Logic-in-Memory Architecture

Memristor-based logic-in-memory (LIM) is an emerging technology that enables logic operations within memory, making it a promising solution for data-intensive applications. LIM architectures have different types according to where computations are executed, with each type being suitable for specific design objectives and application domains. However, mapping applications to a single LIM mode restricts the full utilization of different LIM modes. In this paper, we propose LIM-GEN, a data-guided framework for automated generation of heterogeneous LIM architectures. To take advantages of different LIM modes, three LIM modes are combined and used as building blocks to create heterogeneous architectures. Given the data-centric nature and large design space, there is an urgent need of developing new EDA tools for synthesizing such LIM architectures. LIM-GEN includes an automatic hardware synthesis flow, which takes behavior-level descriptions as input to generate application-specific architectures and dataflows. During synthesis, data distribution, task allocation and crossbar mapping are optimized through a design space exploration process. We evaluate LIM-GEN in several data-intensive applications and compare the generated heterogeneous architectures with synthesized architectures with a single LIM mode. The experimental results demonstrate significant improvements in latency, area and power consumption, brought by the heterogeneous architectures generated by LIM-GEN.