论文标题
基于熵的建模，用于估算软错误对二进制神经网络推断的影响
Entropy-Based Modeling for Estimating Soft Errors Impact on Binarized Neural Network Inference
论文作者
论文摘要
在过去的几年中，大规模数据集的易于访问性极大地改变了范式，以开发由神经网络（NN）驱动的高度准确预测模型。这些模型可能会受到辐射诱导的瞬态断层的影响，这可能会导致长期运行的预期NN推理加速器的逐渐降级。我们对NN推理加速器的严格脆弱性评估的关键观察表明，重量和激活函数对单事件不满（SEU）和多位数不满（MBU）（尤其是在我们选择的卷积神经网络的前五层中）都易于显着。在本文中，我们介绍了相对准确的统计模型，以划定跨层和所选NN的每一层的SEU和MBU的影响。这些模型可用于评估NN拓扑的误差幅度，然后再在安全至关重要的应用中采用它们。
Over past years, the easy accessibility to the large scale datasets has significantly shifted the paradigm for developing highly accurate prediction models that are driven from Neural Network (NN). These models can be potentially impacted by the radiation-induced transient faults that might lead to the gradual downgrade of the long-running expected NN inference accelerator. The crucial observation from our rigorous vulnerability assessment on the NN inference accelerator demonstrates that the weights and activation functions are unevenly susceptible to both single-event upset (SEU) and multi-bit upset (MBU), especially in the first five layers of our selected convolution neural network. In this paper, we present the relatively-accurate statistical models to delineate the impact of both undertaken SEU and MBU across layers and per each layer of the selected NN. These models can be used for evaluating the error-resiliency magnitude of NN topology before adopting them in the safety-critical applications.
