RRAM-Based in-Memory Computing for Intelligent Applications
The rapid advancement of artificial intelligence has fueled a heightened demand for highly energy-efficient chips. Nevertheless, conventional von Neuman computation architectures encounter critical bandwidth bottlenecks when handling vast amounts of real-time data processing. In-memory computing (IMC) paradigms leveraging resistive random-access memory (RRAM) have demonstrated remarkable potential in enabling these burgeoning intelligent applications. In this paper, we have demonstrated our efforts on RRAM-based IMC systems, encompassing the design of RRAM devices and the implementation of RRAM-based IMC chips. We envision these works may set the stage for future applications in RRAM in-memory computing technology.
Authors
- Cuimei Wang
- Ru Huang (ORCID: https://orcid.org/0000-0001-7545-0987)
- Qishen Wang (ORCID: https://orcid.org/0000-0002-4013-6426)
- Qilin Zheng
- Shengyu Bao (ORCID: https://orcid.org/0000-0001-7848-5958)
- Lin Bao (ORCID: https://orcid.org/0000-0002-3146-6124)
- Zongwei Wang (ORCID: https://orcid.org/0000-0001-6297-2700)
- Linbo Shan
- Jingwei Sun (ORCID: https://orcid.org/0009-0007-7888-0625)
- Yujie Chen (ORCID: https://orcid.org/0009-0004-3987-6138)
- Yimao Cai (ORCID: https://orcid.org/0000-0003-2961-9176)
- Yuhang Yang (ORCID: https://orcid.org/0009-0002-1220-5524)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Peking University (CN)
- State Key Laboratory of Information Photonics and Optical Communications
Publication Details
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1109/cstic64481.2025.11017918
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Natural Science Foundation of China
- Beijing Nova Program