MemMIMO: A Simulation Framework for Memristor-Based Massive MIMO Acceleration

作者：Jiawei Xu, Yi Zheng, Dimitrios Stathis, Ruijia Wang, Ruisi Shen, Li‐Rong Zheng, Zhuo Zou, Ahmed Hemani · 发表于：IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 年份：2025 · DOI：10.1109/tcad.2025.3565478 · 被引用次数：2 · 研究领域：Advanced Memory and Neural Computing、Energy Harvesting in Wireless Networks、Quantum-Dot Cellular Automata

Memristor-based crossbar architectures have proven highly effective for matrix vector multiplication (MVM) operations, making them a promising solution for accelerating the MVMs widely used in precoding algorithms for multiple input multiple output (MIMO) wireless communication systems. However, real-world implementation of memristor-based computing systems face challenges due to commonly observed non-idealities in both the devices themselves and the circuits they’re built into. To facilitate a rapid design flow and investigate the impact of non-idealities, an integrated open-source simulation framework MemMIMO is developed. The simulation framework estimates the accuracy and hardware performance of the computing system, offering a variety of flexible design options. MemMIMO integrates a behavioral model of the mix-signal architecture with a digital front-end. There are three major building blocks in MemMIMO: the device fitting block, the mapping block, and the performance estimation block. These blocks work together to map the complex MVMs in precoding algorithms for MIMO systems to crossbar-based architectures that incorporate memristor models characterized by physical device behavior. Using two typical use cases targeting six-generation (6G) massive MIMO communication as case studies, MemMIMO is used to model different memristor devices, explore the impact of non-idealities on system accuracy, and benchmark circuit-level performance metrics including area, speed, and power...