Stuck-at-Fault Immunity Enhancement of Memristor-Based Edge AI Systems
Stuck-at-Fault Immunity Enhancement of Memristor-Based Edge AI Systems
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DOI：
10.1109/jetcas.2022.3207687
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发表时间：
2022-12
影响因子：
4.6
通讯作者：
Md. Oli-Uz-Zaman;Saleh Ahmad Khan;W. Oswald;Zhiheng Liao;Jinhui Wang
中科院分区：
文献类型：
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作者：
Md. Oli-Uz-Zaman;Saleh Ahmad Khan;W. Oswald;Zhiheng Liao;Jinhui Wang
深度神经网络（DNN）广泛应用于边缘人工智能领域。但复杂的感知和决策需要超大的计算量，使得DNN架构非常复杂。忆阻器具有多级电阻特性，可以实现更快的内存中 DNN 计算，从而消除冯诺依曼架构和 CMOS 技术造成的瓶颈。然而，忆阻器因制造不成熟和大量器件使用而产生的卡故障（SAF）缺陷使得基于忆阻器的边缘人工智能无法商业化。为了缓解这个问题，本文提出了自适应映射方法（AMM）。基于对 CIFAR10 数据集的 VGG8 模型的分析，实验结果表明，在 SAF 从 0.1% 到 50% 的情况下，AMM 可以有效地将推理精度恢复到 90%（没有 SAF 的原始精度），其中 Stuck-at-One (SA1): Stuck-at-Zero (SA0) = 5:1、1:5 和 1:1。此外，AMM 对非线性和电导漂移具有显着的抗扰性。在存在高非线性 LTP = 4 和 LTD = −4 的情况下，AMM 将精度提高了 50% 以上，并且标准电导漂移（85 摄氏度下 10 年）几乎对 AMM 边缘 AI 中 DNN 的推理精度没有影响。
Deep Neural Networks (DNNs) are widely used in edge AI. But the complex perception and decision-making demands the overlarge computation and makes the DNN architecture very sophisticated. Memristors have multilevel resistance property that enables faster in-memory DNN computation to remove the bottleneck caused by the von Neumann architecture and CMOS technology. However, the Stuck-at-Fault (SAF) defect of memristor generated from immature fabrication and heavy device utilization makes the memristor-based edge AI commercially unavailable. To mitigate this problem, an Adaptive Mapping Method (AMM) is proposed in this paper. Based on the analysis for the VGG8 model with CIFAR10 dataset, the experiment results show that the AMM is efficient in restoring the inference accuracy up to 90% (the original accuracy without SAF) under SAFs from 0.1% to 50%, where Stuck-at-One (SA1): Stuck-at-Zero (SA0) = 5:1, 1:5, and 1:1. Additionally, the AMM has a significant immunity against the nonlinearity and conductance drift. The AMM improves the accuracy more than 50% in presence of high nonlinearity LTP = 4 and LTD = −4 and the standard conductance drift (10 years at 85 degree centigrade) nearly has no influence on the inference accuracy of the DNN in edge AI with the AMM.