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An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior
- Source :
- Neuromorphic Computing and Engineering, Vol 3, Iss 1, p 014011 (2023)
- Publication Year :
- 2023
- Publisher :
- IOP Publishing, 2023.
-
Abstract
- The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices’ retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.
Details
- Language :
- English
- ISSN :
- 26344386
- Volume :
- 3
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Neuromorphic Computing and Engineering
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.21d7c79f2c69440f80d4473c64eb1b4f
- Document Type :
- article
- Full Text :
- https://doi.org/10.1088/2634-4386/acb965