用多层机器学习加速代码调优，减少无效尝试，提升效率。
ML$^2$Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models
- 引入有效性预测模型过滤无效配置，避免错误发生。
- 仅需12.3%样本即达TVMe性能，无效调优减少60.8%。
- 适合深度学习加速器优化，尤其关注调优效率的开发者。
深度学习模型复杂度不断提升，亟需软硬件协同优化，尤其是针对深度学习加速器。现有自动调优方法常因对无效配置进行性能分析而导致调优时间过长，甚至引发运行时错误。本文提出ML²Tuner，一种基于多层级机器学习的调优技术，通过引入有效性预测模型提前筛选无效配置，并结合编译过程中的隐含特征构建高性能预测模型。在扩展版VTA加速器上的实验表明，ML²Tuner仅需与TVM相当方法12.3%的样本即可实现相当的性能提升，且无效配置分析尝试平均减少60.8%，显著提升了调优效率。
原文摘要 · Abstract (English)
The increasing complexity of deep learning models necessitates specialized hardware and software optimizations, particularly for deep learning accelerators. Existing autotuning methods often suffer from prolonged tuning times due to profiling invalid configurations, which can cause runtime errors. We introduce ML$^2$Tuner, a multi-level machine learning tuning technique that enhances autotuning efficiency by incorporating a validity prediction model to filter out invalid configurations and an advanced performance prediction model utilizing hidden features from the compilation process. Experimental results on an extended VTA accelerator demonstrate that ML$^2$Tuner achieves equivalent performance improvements using only 12.3% of the samples required with a similar approach as TVM and reduces invalid profiling attempts by an average of 60.8%, Highlighting its potential to enhance autotuning performance by filtering out invalid configurations
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。