Before revision:  On real systems for both image and speech , ALERT achieves close-to-optimal results. Comparing with the optimal static DNN-model and power-limit setting, which is impractical to predict, ALERTachieves a harmonic mean 33\% of energy savings while satisfying accuracy constraints, and reduces image-classification error rate by 58\% and sentence-prediction perplexity by 52\% while satisfying energy constraints .
After revision:  On real systems for both image and speech tasks in dynamic environments. ALERT's holistic approach achieves more than 13\% energy reduction, and 27\% error reduction over prior approaches that adapt solely at the application or system level. Furthermore, ALERT incurs only 3\% more energy consumption and 2\% higher DNN-inference error than an oracle scheme with perfect application and system knowledge .