MemMIMO: A Simulation Framework for Memristor-Based Massive MIMO Acceleration
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.
Authors
- Ahmed Hemani (ORCID: https://orcid.org/0000-0003-0565-9376)
- Zhuo Zou (ORCID: https://orcid.org/0000-0002-8546-1329)
- Dimitrios Stathis (ORCID: https://orcid.org/0000-0002-5697-4272)
- Jiawei Xu (ORCID: https://orcid.org/0000-0002-6192-558X)
- Li‐Rong Zheng (ORCID: https://orcid.org/0000-0001-9588-0239)
- Yi Zheng (ORCID: https://orcid.org/0000-0001-5547-0956)
- Ruijia Wang (ORCID: https://orcid.org/0000-0002-4211-5207)
- Ruisi Shen
Institutions
- University of Macau (MO)
- Fudan University (CN)
- Institute of Microelectronics (CN)
- KTH Royal Institute of Technology (SE)
Publication Details
- Journal
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1109/tcad.2025.3565478
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00