This perspective article argues that hyperparameters such as learning rate, batch size, numerical precision, and training workers are key determinants of energy scalability in CNN training. These parameters directly influence convergence dynamics, hardware utilization, and training duration, leading to substantially different energy profiles even when comparable accuracy is achieved. Moreover, hyperparameter search itself introduces a significant cumulative energy cost, often exceeding that of the final selected model. By analyzing the interaction between convergence behavior and energy consumption, this work highlights the need to treat energy as an explicit scalability metric and to integr... More >