The widespread adoption of deep neural networks in safety-critical systems necessitates the examination of the safety issues raised by hardware errors. The appropriateness of the concern is herein confirmed by evidencing the possible catastrophic impact of hardware bit errors on DNN accuracy. The consequent interest in fault tolerance methods that are comprehensive yet low-cost to match the margin requirements of consumer deep learning applications can be met through a rigorous exploration of the mathematical properties of the deep neural network computations. Our novel technique, Sanity-Check , allows error detection in fully-connected and convolutional layers through the use of linear algorithmic checksums. The purely software-based implementation of Sanity-Check facilitates the widespread adoption of our technique on a variety of off-the-shelf execution platforms while requiring no hardware modification. We further propose a dedicated hardware unit that seamlessly integrates with modern deep learning accelerators and eliminates the performance overhead of the software-based implementation at the cost of a negligible area and power budget in a DNN accelerator. Sanity-Check delivers perfect critical error coverage in our error injection experiments and offers a promising alternative for low-cost error detection in safety-critical deep neural network applications.

中文翻译：

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具有线性算法校验和的深度神经网络加速器中的低成本错误检测

在安全关键系统中广泛采用深度神经网络需要检查硬件错误引起的安全问题。此处通过证明硬件位错误对 DNN 准确性可能造成的灾难性影响来证实该担忧的适当性。通过对深度神经网络计算的数学特性的严格探索，可以满足对全面但低成本以匹配消费者深度学习应用程序的裕度要求的容错方法的兴趣。我们的新技术 Sanity-Check 允许通过使用线性算法校验和在全连接层和卷积层中进行错误检测。Sanity-Check 的纯基于软件的实现促进了我们的技术在各种现成的执行平台上的广泛采用，而无需修改硬件。我们进一步提出了一个专用硬件单元，它与现代深度学习加速器无缝集成，并以 DNN 加速器中可忽略的面积和功率预算为代价，消除了基于软件的实现的性能开销。Sanity-Check 在我们的错误注入实验中提供了完美的关键错误覆盖，并为安全关键的深度神经网络应用中的低成本错误检测提供了一个有前途的替代方案。我们进一步提出了一个专用硬件单元，它与现代深度学习加速器无缝集成，并以 DNN 加速器中可忽略的面积和功率预算为代价，消除了基于软件的实现的性能开销。Sanity-Check 在我们的错误注入实验中提供了完美的关键错误覆盖，并为安全关键的深度神经网络应用中的低成本错误检测提供了一个有前途的替代方案。我们进一步提出了一个专用硬件单元，它与现代深度学习加速器无缝集成，并以 DNN 加速器中可忽略的面积和功率预算为代价，消除了基于软件的实现的性能开销。Sanity-Check 在我们的错误注入实验中提供了完美的关键错误覆盖，并为安全关键的深度神经网络应用中的低成本错误检测提供了一个有前途的替代方案。