Explain the trade-offs between pruning and quantization in deep learning model compression, detailing specific scenarios where one technique might be favored over the other.

Pruning and quantization are two key techniques for deep learning model compression, each with distinct trade-offs and suitability for different scenarios. Pruning aims to reduce the model size by removing redundant or less important connections (weights) in the neural network, while quantization reduces the precision of the weights and activations, thereby decreasing the memory footprint and computational cost. The primary trade-off lies in the impact on accuracy versus compression ratio and hardware compatibility. Pruning, especially unstructured pruning where individual weights are removed, can achieve high compression ratios without significant accuracy loss, provided it is done carefully. However, unstructured pruning often leads to irregular memory access patterns, which may not be efficiently supported on all hardware platforms, particularly standard CPUs and GPUs. Sparse matrices resulting from unstructured pruning require specialized hardware or software libraries to realize their full performance benefits. Structured pruning, where entire filters or channels are removed, results in more regular memory access patterns and better hardware compatibility but typically achieves lower compression ratios than unstructured pruning for the same level of accura....