迁移学习赋能CNN：面向ReRAM潜路径干扰的高效数据检测方法

戴彬; 吴安妮

doi:10.11999/JEIT260354

迁移学习赋能CNN：面向ReRAM潜路径干扰的高效数据检测方法

doi: 10.11999/JEIT260354 cstr: 32379.14.JEIT260354

戴彬^,,
吴安妮

南京邮电大学南京 210003

基金项目: 国家自然科学基金项目(62201283)

详细信息

作者简介:
戴彬：男，副教授，研究方向为无线通信与存储系统中的编码与信号处理

吴安妮：女，硕士研究生，研究方向为存储系统中的信号处理

通讯作者:
戴彬　daibin@njupt.edu.cn

中图分类号: TN492; TP301.1
计量
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- 被引次数: 0
出版历程
- 修回日期: 2026-05-15
- 录用日期: 2026-05-15
- 网络出版日期: 2026-06-02

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

DAI Bin^,,
WU Anni

Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China under Grant 62201283

摘要

摘要: 阻变存储器中的SPI会引发不可预测的单元间相关性，显著提高信号检测的复杂度。传统检测方法通常依赖已知的信道噪声状态假设，在实际应用中泛化能力有限。为解决这一问题，提出了三种基于卷积神经网络(CNN)的数据检测方法，无需依赖先验信道信息即可有效建模并抑制干扰：其一，结合约束编码与多层CNN，通过约束编码判断潜路径干扰状态并进行数据恢复；其二，采用双CNN框架，首先利用轻量CNN进行SPI识别，再通过多层CNN实现精细化检测；其三，引入迁移学习机制，在保持检测精度的同时，将所需训练样本规模降低至传统方法的千分之一。仿真结果表明，所提方法在未知信道状态下均实现误码率性能的显著提升，误比特率相比现有算法至少降低一半，逼近性能极限。此外，迁移学习机制的引入将训练样本需求由$ {10}^{6} $组降至$ 1000 $组，减少三个数量级。
- 阻变存储器 /
- SPI /
- 数据检测 /
- 卷积神经网络 /
- 迁移学习
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

HTML全文

图 1 4×4 ReRAM阵列中SPI的示意图

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图 2 多层选择性卷积核CNN网络的架构图

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图 3 双卷积神经网络辅助数据检测

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图 4 不同检测器的误比特率性能对比

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图 5 CNN网络消融实验的误比特率性能对比

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图 6 迁移学习目标域中不同样本数量下的误比特率性能对比

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表 1 测试时间对比

算法 DNN检测 BP检测 BiLSTM检测 Type-I/II检测(无SK) Type-I/II检测(有SK)

时间(s) 15 99 21 10/17 34/43

下载: 导出CSV

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