永久性故障下近似DNN的可靠性退化及其缓解
Exposing Reliability Degradation and Mitigation in Approximate DNNs under Permanent Faults
摘要：
已知近似计算通过引入具有可容忍的准确度损失的不精确性来增强深度神经网络加速器的能量效率。然而，小的精度变化可能会增加这些加速器对不期望的细微干扰（例如永久性故障）的灵敏度。永久性故障的影响，准确的深度神经网络（AccDNN）加速器已在文献中进行了彻底的研究。相反，永久性故障的影响及其在近似DNN（AxDNN）加速器中的缓解措施还远远没有得到充分的探索。为此，我们首先使用GPU和TPU加速器中最先进的Evoaprox 8b乘法器对近似多层感知器（MLP）和卷积神经网络（CNN）进行了广泛的故障恢复分析。然后，我们提出了一种新的故障缓解方法，即，权重的故障感知重调（Fal-reTune）。Fal-reTune在存在故障的情况下使用权重映射函数重新调整权重，以提高分类精度。为了评估我们提出的缓解方法的故障恢复能力和有效性，我们使用了最广泛使用的MNIST，Fashion-MNIST和CIFAR 10数据集。我们的研究结果表明，与AccDNN加速器相比，永久性故障加剧了AxDNN的准确性损失。例如，AxDNN中的永久性故障可能导致56%的准确度损失，而AccDNN加速器中相同的故障位可能仅导致4%的准确度损失。我们的经验表明，我们提出的Fal-reTune缓解方法将AxDNN的性能提高了98%，即使故障率高达50%。此外，我们观察到AxDNN中的故障恢复能力与其能量效率正交。
英文摘要：
Approximate computing is known for enhancing deep neural network accelerators' energy efficiency by introducing inexactness with a tolerable accuracy loss. However, small accuracy variations may increase the sensitivity of these accelerators towards undesired subtle disturbances, such as permanent faults. The impact of permanent faults in accurate deep neural network (AccDNN) accelerators has been thoroughly investigated in the literature. Conversely, the impact of permanent faults and their mitigation in approximate DNN (AxDNN) accelerators is vastly under-explored. Towards this, we first present an extensive fault resilience analysis of approximate multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) using the state-of-the-art Evoapprox8b multipliers in GPU and TPU accelerators. Then, we propose a novel fault mitigation method, i.e., fault-aware retuning of weights (Fal-reTune). Fal-reTune retunes the weights using a weight mapping function in the presence of faults for improved classification accuracy. To evaluate the fault resilience and the effectiveness of our proposed mitigation method, we used the most widely used MNIST, Fashion-MNIST, and CIFAR10 datasets. Our results demonstrate that the permanent faults exacerbate the accuracy loss in AxDNNs compared to the AccDNN accelerators. For instance, a permanent fault in AxDNNs can lead to 56\% accuracy loss, whereas the same faulty bit can lead to only 4\% accuracy loss in AccDNN accelerators. We empirically show that our proposed Fal-reTune mitigation method improves the performance of AxDNNs up to 98%, even with fault rates of up to 50%. Furthermore, we observe that the fault resilience in AxDNNs is orthogonal to their energy efficiency.
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