ONNXPruner:基于ONNX的通用模型修剪适配器
概括
ONNXPruner通过调整算法以适应ONNX模型来增强模型修剪. 它的节点关联树和树级评估可以在不同的平台上提高修剪效率,而不需要额外的操作.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 模型修剪的进步集中在算法和基准上.
- 跨多种模型和平台的实际应用是具有挑战性的.
研究的目的:
- 介绍ONNXPruner,这是一个适合ONNX格式模型的多功能适配器.
- 在深度学习框架和硬件中简化修剪适应.
主要方法:
- 利用节点关联树来自动适应模型架构.
- 采用树级评估方法进行综合分析.
- 通过澄清结构关系和对相互连接节点的影响来指导修剪.
主要成果:
- 在各种模型和数据集中,ONNXPruner表现出强大的适应性.
- 树级评估可以提高修剪性能,而无需额外的计算成本.
- 节点关联树有效指导修剪过程.
结论:
- ONNXPruner促进了模型修剪的实际应用.
- 提出的方法提升了修剪技术的有效性和适应性.
- 这项工作弥合了修剪研究和现实世界部署之间的差距.
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