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首页> 外文会议>2019 56th ACM/IEEE Design Automation Conference >DRIS-3: Deep Neural Network Reliability Improvement Scheme in 3D Die-Stacked Memory based on Fault Analysis
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DRIS-3: Deep Neural Network Reliability Improvement Scheme in 3D Die-Stacked Memory based on Fault Analysis

机译：DRIS-3：基于故障分析的3D叠层存储器中的深度神经网络可靠性改进方案

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Various studies have been carried out to improve the operational efficiency of the Deep Neural Networks (DNNs). However, the importance of the reliability in DNNs has generally been overlooked. As the underlying semiconductor technology decreases in reliability, the probability that some components of computing devices fail also increases, preventing high accuracy in DNN operations. To achieve high accuracy, ensuring operational reliability, even if faults occur, is necessary.In this paper, we introduce a DNN reliability improvement scheme in 3D die-stacked memory called DRIS-3, based on the correlation between the faults in weights and an accuracy loss. We analyze the fault characteristics of conventional DNN models to find the bits that cause significant accuracy loss when faults are injected into weights. On the basis of the findings, we propose a reliability improvement structure which can reduce faults on the bits that must be protected for accuracy, considering asymmetric soft error rate (SER) per layer in 3D die-stacked memory. Experimental results show that with the proposed method, the fault tolerance is improved regardless of the type of model and the pruning applied. The fault tolerance based on bit error rate (BER) for a 1% accuracy loss is increased up to 104 times over the conventional model. CCS CONCEPTS • Computer systems organization → Neural networks; • Hardware → Fault tolerance;
机译：为了提高深度神经网络（DNN）的运行效率，已经进行了各种研究。但是，DNN中可靠性的重要性通常被忽略。随着基础半导体技术可靠性的降低，计算设备的某些组件发生故障的可能性也随之增加，从而阻止了DNN操作的高精度。为了实现高精度，即使出现故障，也必须确保操作可靠性。本文基于权重与故障之间的相关性，在3D芯片堆叠存储器DRIS-3中引入了DNN可靠性改进方案。准确性损失。我们分析了传统DNN模型的故障特征，以查找将故障注入权重时会导致严重的精度损失的位。基于这些发现，我们提出了一种可靠性改进结构，考虑到3D芯片堆叠存储器中每层的不对称软错误率（SER），可以减少必须保护精度的位上的错误。实验结果表明，该方法无论模型类型和修剪方式如何，均能提高容错能力。基于1％精度损失的误码率（BER）的容错能力提高到10 \ n 4 \ n倍于常规模型。 CCS概念•计算机系统组织→神经网络； •硬件→容错；

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