Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
#pruning #quantization #model compression #neural networks #compression order #joint compression #machine learning
📌 Key Takeaways
- The study investigates the impact of compression order on model performance in joint pruning and quantization.
- It compares two approaches: pruning before quantization and quantization before pruning.
- Findings reveal that the order significantly affects model accuracy and efficiency.
- The research provides guidelines for optimal compression strategies in neural networks.
📖 Full Retelling
arXiv:2603.18426v1 Announce Type: new
Abstract: What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve higher efficiency by combining multiple methods such as pruning and quantization. A central but underexplored factor in joint model compression is the compression order, or the sequence of different methods within the compression pipeline. Most prior studies have either s
🏷️ Themes
Model Compression, Neural Networks
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Original Source
arXiv:2603.18426v1 Announce Type: new
Abstract: What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve higher efficiency by combining multiple methods such as pruning and quantization. A central but underexplored factor in joint model compression is the compression order, or the sequence of different methods within the compression pipeline. Most prior studies have either s
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