Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression
📰 ArXiv cs.AI
Prune-Quantize-Distill is a pipeline for efficient neural network compression, improving inference time under CPU and memory constraints
Action Steps
- Prune the neural network to reduce parameters and computations
- Quantize the pruned network to reduce memory usage and improve execution efficiency
- Distill the quantized network to retain accuracy and further reduce size
Who Needs to Know This
ML researchers and engineers benefit from this pipeline as it enables efficient deployment of neural networks, while software engineers and DevOps teams can utilize the compressed models for faster inference
Key Insight
💡 Unstructured sparsity can reduce model storage but may not accelerate CPU execution due to irregular memory access and sparse kernel overhead
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🚀 Prune-Quantize-Distill: efficient neural network compression pipeline for faster inference 🚀
Key Takeaways
Prune-Quantize-Distill is a pipeline for efficient neural network compression, improving inference time under CPU and memory constraints
Full Article
Title: Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression
Abstract:
arXiv:2604.04988v1 Announce Type: cross Abstract: Modern deployment often requires trading accuracy for efficiency under tight CPU and memory constraints, yet common compression proxies such as parameter count or FLOPs do not reliably predict wall-clock inference time. In particular, unstructured sparsity can reduce model storage while failing to accelerate (and sometimes slightly slowing down) standard CPU execution due to irregular memory access and sparse kernel overhead. Motivated by this ga
Abstract:
arXiv:2604.04988v1 Announce Type: cross Abstract: Modern deployment often requires trading accuracy for efficiency under tight CPU and memory constraints, yet common compression proxies such as parameter count or FLOPs do not reliably predict wall-clock inference time. In particular, unstructured sparsity can reduce model storage while failing to accelerate (and sometimes slightly slowing down) standard CPU execution due to irregular memory access and sparse kernel overhead. Motivated by this ga
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