The insights from the proposed lightweight inference scheme can be applied to improve the energy efficiency of other machine learning models and applications by focusing on optimizing computational resources during inference. By incorporating similar confidence-based mechanisms to determine the depth or complexity of the inference process, models can dynamically adjust their computational requirements based on the input data. This adaptive approach can lead to significant energy savings by reducing unnecessary computations for confident predictions and allocating resources more efficiently. Furthermore, the concept of lightweight inference can be extended to various domains beyond neural networks, such as traditional machine learning algorithms and IoT devices, to enhance performance while minimizing energy consumption.