Transfer Learning Aided CNN for Efficient Data Detection in ReRAM with Sneak-Path Interference

DAI Bin; WU Anni

doi:10.11999/JEIT260354

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DAI Bin, WU Anni. Transfer Learning Aided CNN for Efficient Data Detection in ReRAM with Sneak-Path Interference[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260354

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DAI Bin, WU Anni. Transfer Learning Aided CNN for Efficient Data Detection in ReRAM with Sneak-Path Interference[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260354

DAI Bin, WU Anni. Transfer Learning Aided CNN for Efficient Data Detection in ReRAM with Sneak-Path Interference[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260354

Citation:

DAI Bin, WU Anni. Transfer Learning Aided CNN for Efficient Data Detection in ReRAM with Sneak-Path Interference[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260354

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Transfer Learning Aided CNN for Efficient Data Detection in ReRAM with Sneak-Path Interference

doi: 10.11999/JEIT260354 cstr: 32379.14.JEIT260354

DAI Bin^,,
WU Anni

Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (62201283)

Received Date: 2026-03-26
Accepted Date: 2026-05-15
Rev Recd Date: 2026-05-15

Available Online: 2026-06-02

Abstract

Abstract

Objective Sneak Path Interference (SPI) in Resistive Random-Access Memory (ReRAM) introduces unpredictable inter-cell correlations and increases the complexity of signal detection. Conventional detection methods usually rely on known channel noise states, which limits their generalization in practical applications. To address this issue, three data detection methods based on Convolutional Neural Networks (CNNs) are proposed to model and suppress interference without prior channel information. The first method combines constrained coding with a multilayer CNN to determine the SPI state and recover data. The second method uses a dual-CNN framework, in which a lightweight CNN first identifies SPI and a multilayer CNN then performs refined detection. The third method incorporates Transfer Learning (TL) to maintain detection accuracy while reducing the required training sample size to one thousandth of that required by conventional methods. Simulation results show that the proposed methods achieve improved Bit Error Rate (BER) performance under unknown channel conditions. Compared with existing algorithms, the BER is reduced by at least half and approaches the theoretical performance limit. In addition, TL reduces the required number of training samples from$ {10}^{6} $ to $ 1\;000 $, corresponding to a reduction of three orders of magnitude. Methods To address different challenges in SPI detection, this paper proposes three methods. First, the Constrained Coding-Aided CNN (CC-CNN) detection framework is developed to handle complex inter-cell correlations caused by SPI. This method first uses constrained coding to detect the presence of interference. It then uses a CNN to learn random correlations under interference and achieve accurate signal recovery. Second, the dual-CNN-based detection method is proposed to avoid the code-rate loss caused by conventional constrained coding. By directly using a CNN to learn and identify SPI patterns from raw data, this method removes the need for redundant coding or extra overhead. It achieves high-precision interference detection while preserving the overall code-rate performance of the system. Third, the TL-based CNN (TL-CNN) detection method is designed to reduce the dependence of high-performance CNNs on large training datasets. By reusing knowledge from pretrained models, this method enables rapid adaptation to ReRAM signal detection tasks. It reduces the required number of training samples while maintaining high detection accuracy and resource efficiency, thereby improving its feasibility in practical scenarios. Results and Discussions Simulation results show that the three proposed methods consistently approach the theoretical lower bound (Fig. 4). They outperform baseline methods, including the Belief Propagation (BP) detector, Deep Neural Network (DNN) detector, and Elementary Signal Estimator (ESE) detector. The two-step network achieves performance comparable to that of the single-step network while avoiding code-rate loss. The TL-CNN detector attains near-optimal BER with only 1,000 target-domain samples, and its performance stabilizes when the sample size exceeds 1,000 (Fig. 6), confirming its data efficiency. The integration of the Selective Kernel (SK) module enables the models to capture SPI-induced spatial correlations effectively (Fig. 5). The TL strategy also supports robust performance under different noise conditions. Conclusions The crossbar array architecture of ReRAM is susceptible to SPI during storage operations, reducing data reliability. To address this issue, this paper proposes three deep learning-based detection methods. Type-I integrates constrained coding with a CNN for efficient and fast interference detection. Type-II uses a two-stage processing strategy. It first classifies interference patterns in the memory array and then detects affected units, thereby maintaining high detection accuracy while minimizing code-rate loss. Type-III introduces a TL framework that uses a pretrained source-domain model to reduce the number of target-domain training samples and lower training overhead. Experimental results show that, under different noise conditions, all three proposed methods achieve performance close to the theoretical lower bound. These methods provide an effective solution for improving the reliability of ReRAM storage systems.
- ReRAM,
- Sneak path interference,
- Data detection,
- Convolutional neural network,
- Transfer learning

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References(28)

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