Can knowledge distillation reduce computational power consumption?

Yes, knowledge distillation can effectively reduce computational power consumption, particularly during model inference. The technique achieves this by training a smaller, computationally cheaper student model to mimic the knowledge of a larger, more complex teacher model.

The primary mechanism for reduced computation is model compression. The student model typically has fewer parameters and simpler operations than the teacher, inherently requiring less computation per prediction. Knowledge distillation focuses on transferring the teacher's learned function mapping (captured in its softened output probabilities/logits or intermediate representations) rather than requiring the student to learn complex patterns independently from scratch. Reduced computation leads to faster inference times and lower energy requirements, especially on resource-constrained devices.

This reduction in computational demand is most valuable in deployment scenarios. It enables deploying high-performing models onto devices with limited processing power (edge devices, mobile phones, IoT) and scales efficiently in cloud environments by lowering the cost per inference. The key benefit lies in achieving performance close to the large teacher model while using a fraction of the computational resources during the critical inference phase.