This research challenges the common assumption that sparsity and quantization techniques can be combined independently in model compression.

Key Findings:

• Sparsity and quantization are not orthogonal compression methods
• Their interaction significantly affects model performance
• Proper sequencing and coordination of these techniques is critical
• Developers can achieve better compression ratios by understanding their interplay

For engineering teams, these insights enable more efficient deployment of large neural networks on resource-constrained devices, potentially reducing computational and memory requirements while maintaining accuracy.

Effective Interplay between Sparsity and Quantization: From Theory to Practice