相关实验视频
通过ROFT模型加速调整过程,以优化DNN运营商的ROFT模式
ZiChuan He1, Hui Zhong1, XiaoHua Shi2
1School of Software, Beihang University, Beijing, 100083, China.
Scientific reports
|October 17, 2025
概括
这项研究介绍了ROFT,一种加速深度神经网络 (DNN) 调整的新方法. 在不牺牲DNN性能的情况下,ROFT显著减少了GPU和NPU的优化时间.
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:
- 深度神经网络 (DNN) 需要密集的计算和优化.
- 编译器优化提供了与手动方法相似的性能,但涉及到漫长的调整.
- 现有的优化技术面临着长时间调整的挑战.
研究的目的:
- 提出一种用于加速DNN调过程的新方法.
- 为了显著减少优化时间而不会影响DNN性能.
- 引入成本模型和搜索算法,以实现高效的DNN优化.
主要方法:
- 开发了ROFT (Roofline for Fast AutoTune),一种类似于Roofline的成本模型,用于评估DNN时间表.
- 实现了基于ROFT成本模型的灵活的两阶段搜索算法.
- 在NVIDIA GPU和华为Ascend NPU上评估了ROFT.
主要成果:
- 与现有方法相比,ROFT在NVIDIA GPU上加快了调整过程约4倍,在Huawei Ascend310 NPU上加快了10倍.
- 该方法在典型的深度神经网络中实现了显著的加快速度.
- 一些DNN的推理时间提高了高达7%.
结论:
- 拟议的ROFT方法可以大幅提高DNN调效率.
- ROFT提供了一种切实可行的解决方案,用于减少DNN优化中的计算开销.
- 这种方法可以在各种硬件上更快地部署优化的深度神经网络.
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