Assess students' ability to design and evaluate compression strategies—pruning, quantization, and knowledge distillation—for deploying deep neural networks on resource-constrained edge devices. Scope includes algorithm selection, hardware-aware trade-offs (accuracy, latency, memory, energy), evaluation metrics and benchmarks, pipeline implementation and hyperparameter tuning, and justification of deployment decisions based on experimental results.