标题： 基于ECC的DNN故障容错方法 显示英文标题

标题： An ECC-based Fault Tolerance Approach for DNNs

摘要： 深度神经网络（DNN）在解决一系列机器学习问题上取得了巨大成功。最近，它们已被部署在数据中心（可能用于商业关键或工业应用）和安全关键系统中，例如自动驾驶汽车。因此，在DNN参数存储在内存中的潜在位翻转错误存在的情况下，其正确功能在其在安全关键应用中的适用性中起着关键作用。在本文中，提出了一种基于纠错码（ECC）的容错方法，称为SPW，以确保在存在位翻转故障的情况下DNN的正确功能。在所提出的方法中，通过存储的ECC检测错误发生，并在单个位错误的情况下进行纠正，否则权重将完全设置为零（即被屏蔽）。提出并利用了一种统计故障注入活动来研究所提出方法的有效性。实验结果表明，在存在比特错误率为10^(-1)的情况下，与应用ECC技术的情况相比，DNN的准确性提高了300%以上，而仅增加了47.5%的面积开销。 显示英文摘要

摘要： Deep Neural Network (DNN) has achieve great success in solving a wide range of machine learning problems. Recently, they have been deployed in datacenters (potentially for business-critical or industrial applications) and safety-critical systems such as self-driving cars. So, their correct functionality in the presence of potential bit-flip errors on DNN parameters stored in memories plays the key role in their applicability in safety-critical applications. In this paper, a fault tolerance approach based on Error Correcting Codes (ECC), called SPW, is proposed to ensure the correct functionality of DNNs in the presence of bit-flip faults. In the proposed approach, error occurrence is detected by the stored ECC and then, it is correct in case of a single-bit error or the weight is completely set to zero (i.e. masked) otherwise. A statistical fault injection campaign is proposed and utilized to investigate the efficacy of the proposed approach. The experimental results show that the accuracy of the DNN increases by more than 300% in the presence with Bit Error Rate of 10^(-1) in comparison to the case where ECC technique is applied, in expense of just 47.5% area overhead.