Assess students' ability to apply pruning, quantization, and knowledge distillation to design and evaluate compressed deep neural networks for edge devices; prompt scope includes choosing between structured vs. unstructured pruning, post‑training vs. quantization-aware techniques, teacher–student distillation strategies, combined pipelines, evaluation metrics (accuracy, latency, memory, energy), and deployment/toolchain trade-offs for constrained hardware.