对于N:M Sparsity的空间重新参数化
概括
本研究为N:M稀疏性引入了空间再参数化 (SpRe),提高了卷积内核的效率. 在没有额外的计算成本的情况下,SpRe与非结构化的稀疏性性能匹配.
科学领域:
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
- 模型压缩压缩模型
背景情况:
- N:M稀疏性提供了结构化的压缩,但具有有限的空间稀疏性变化.
- 非结构化的稀疏性提供了更大的空间稀疏性变化,这对性能至关重要.
- 现有的N:M方法很难与非结构化的稀疏性相匹配.
研究的目的:
- 开发一种方法,通过结合非结构化的稀疏度的空间稀疏度分布来增强N:M稀疏度.
- 为了提高N:M稀疏网络的性能,而不会在推断过程中引入额外的计算开销.
主要方法:
- 引入了N:M稀疏性的空间再参数化 (SpRe) 方法.
- 在训练期间使用辅助分支来模拟非结构化的稀疏度的空间分布.
- 开发了一个推断时间重新参数化技术,在不改变N:M稀疏模式或计算成本的情况下合并辅助分支.
主要成果:
- SpRe使N:M稀疏网络能够实现与非结构化的稀疏性类似的空间稀疏性分布.
- 该方法成功匹配了最先进的非结构化稀疏性技术的性能.
- 在各种基准标准中验证了性能,证明了广泛的适用性.
结论:
- SpRe有效地弥合了N:M和非结构化的稀疏性之间的性能差距.
- 拟议的方法通过利用两种稀疏性类型的优势,提供高效的模型压缩.
- 这种技术为优化深度学习模型提供了一个有希望的方向.
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