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 under Grant 62201283

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, significantly increasing the complexity of signal detection. Traditional detection methods typically rely on assumptions about known channel noise states, resulting in limited generalization capability in practical applications. To address this issue, three data detection methods based on convolutional neural networks (CNNs) are proposed, which can effectively model and mitigate interference without relying on prior channel information: first, a method combining constrained coding with a multi-layer CNN, which uses constrained coding to determine the sneak path interference state and recover data; second, a dual-CNN framework that first employs a lightweight CNN for sneak path interference identification, followed by a multi-layer CNN for refined detection; third, an approach incorporating transfer learning, which maintains detection accuracy while reducing the required training sample size to one-thousandth of that of traditional methods. Simulation results demonstrate that the proposed method achieves superior bit error rate (BER) performance under unknown channel conditions, with a BER reduction of at least half relative to existing algorithms, approaching the theoretical performance limit. Moreover, the integration of transfer learning reduces the required training samples from $ {10}^{6} $ to $ 1000 $, corresponding to a reduction of three orders of magnitude. Methods To address distinct challenges in sneak path interference detection, this paper proposes three methods sequentially:1. The integrated constrained coding aided convolutional neural network (CC-CNN) detection framework effectively addresses the complex inter-cell correlations introduced by sneak path interference. This approach first employs constrained coding to detect the presence of interference and subsequently utilizes a CNN to learn and capture the random correlations under the influence of interference, thereby achieving accurate signal recovery.2. The dual-CNN-based detection method resolves the code rate loss associated with traditional constrained coding. By directly leveraging a CNN to learn and identify sneak path interference patterns from raw data, this method eliminates the need for redundant coding or additional overhead. It ensures high-precision interference detection while preserving the overall code rate performance of the system.3. The transfer learning-based CNN (TL-CNN) detection method overcomes the dependence of high-performance CNNs on large-scale training datasets. By reusing knowledge from pre-trained models, this method enables rapid adaptation to ReRAM signal detection tasks. It significantly reduces the required number of training samples while maintaining high detection accuracy and resource efficiency, thereby enhancing the feasibility of the solution in practical scenarios. Results and Discussions Simulation results demonstrate that the performance of the three proposed methods consistently approaches the theoretical lower bound (Fig.6), outperforming baseline methods such as 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 successfully avoiding code rate loss. Notably, the transfer learning-aided CNN attains near-optimal BER with only 1000 target domain samples, and its performance stabilizes when the sample size exceeds 1000 (Fig.7), fully validating its data efficiency. The integration of SK modules enables the models to effectively capture SPI-induced spatial correlations, while the transfer learning strategy ensures the models’ robust performance under different noise conditions. Conclusions The crossbar array architecture of ReRAM is susceptible to sneak-path interference during storage operations, leading to reduced data reliability. To address this issue, this paper proposes three deep learning-based detection methods. Type-I integrates constrained coding with a CNN to achieve efficient and fast interference detection. Type-II adopts a two-stage processing approach: it first classifies interference patterns in the memory array and then performs detection specifically on affected units, thereby ensuring high detection accuracy while minimizing coding rate loss. Type-III introduces a transfer learning framework that leverages a pre-trained model from the source domain, significantly reducing the number of training samples required in the target domain and effectively lowering training overhead. Experimental results show that under different noise conditions, all three proposed methods achieve performance close to the theoretical lower bound, providing an effective solution for enhancing 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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