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ONNXPruner: ONNX-Based General Model Pruning Adapter
Summary
ONNXPruner enhances model pruning by adapting algorithms to ONNX models. Its node association trees and tree-level evaluation improve pruning efficacy across diverse platforms without extra operations.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Model pruning advancements focus on algorithms and benchmarks.
- Practical application across diverse models and platforms is challenging.
Purpose of the Study:
- Introduce ONNXPruner, a versatile adapter for ONNX format models.
- Streamline pruning adaptation across deep learning frameworks and hardware.
Main Methods:
- Utilize node association trees for automatic adaptation to model architectures.
- Employ a tree-level evaluation method for comprehensive analysis.
- Guide pruning by clarifying structural relationships and impact on interconnected nodes.
Main Results:
- ONNXPruner demonstrates strong adaptability across various models and datasets.
- The tree-level evaluation enhances pruning performance without additional computational cost.
- Node association trees effectively guide the pruning process.
Conclusions:
- ONNXPruner facilitates the practical application of model pruning.
- The proposed methods advance the efficacy and adaptability of pruning techniques.
- This work bridges the gap between pruning research and real-world deployment.
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