  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.
