论文标题
EREBA：自适应神经网络的黑盒能量测试
EREBA: Black-box Energy Testing of Adaptive Neural Networks
论文作者
论文摘要
最近，已经针对具有严格的能量限制的嵌入式系统提出了各种深神网络（DNN）模型。与基于准确性的鲁棒性相比，确定DNN在其能耗（能量鲁棒性）方面的鲁棒性（能量鲁棒性）的基本问题相对尚未探索。这项工作研究了适应性神经网络（ADNNS）的能量鲁棒性，这是一种针对许多能量敏感域提出的一种节能DNN，最近获得了吸引力。我们提出了Ereba，这是确定ADNN能量鲁棒性的第一种黑盒测试方法。 Eereba探索并探讨了输入与ADNNS能源消耗之间的关系，以产生能量飙升的样品。使用三个最先进的ADNN进行了广泛的实施和评估，表明，Eereba生成的测试输入可能会大大降低系统的性能。与原始输入相比，EREBA产生的测试输入可以将ADNN的能源消耗增加2,000％。我们的结果还表明，通过EREBA生成的测试输入对于检测能量激增的投入非常有价值。
Recently, various Deep Neural Network (DNN) models have been proposed for environments like embedded systems with stringent energy constraints. The fundamental problem of determining the robustness of a DNN with respect to its energy consumption (energy robustness) is relatively unexplored compared to accuracy-based robustness. This work investigates the energy robustness of Adaptive Neural Networks (AdNNs), a type of energy-saving DNNs proposed for many energy-sensitive domains and have recently gained traction. We propose EREBA, the first black-box testing method for determining the energy robustness of an AdNN. EREBA explores and infers the relationship between inputs and the energy consumption of AdNNs to generate energy surging samples. Extensive implementation and evaluation using three state-of-the-art AdNNs demonstrate that test inputs generated by EREBA could degrade the performance of the system substantially. The test inputs generated by EREBA can increase the energy consumption of AdNNs by 2,000% compared to the original inputs. Our results also show that test inputs generated via EREBA are valuable in detecting energy surging inputs.
