Before revision:  The runtime takes latency, accuracy, and energy constraints , and uses dynamic feedback to predict the best DNN-model and system power-limit setting. The DNN nesting creates a type of flexible network that efficiently delivers a series of results with increasing accuracy as time goes on . These two parts well complement each other: the runtime is aware of the tradeoffs of different DNN settings, and the nested DNNs' flexibility allows the runtime prediction to satisfy application requirements even in unpredictable, changing environments. On real systems for both image and speech , ALERT achieves close-to-optimal results.
After revision:  The runtime takes latency, accuracy, and energy constraints . We evaluate ALERT on CPU and GPU platforms for image and speech , ALERT achieves close-to-optimal results.