Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs used in practice. In the present paper, we start an investigation of predictive models based on machine learning techniques in order to optimize Convolution Neural Networks (CNNs). As a use-case, we focus on the ARM Compute Library which provides three different implementations of the convolution operator at different numeric precision. Starting from a collation of benchmarks, we build and validate models learned by Decision Tree and naive Bayesian classifier. Preliminary experiments on Midgard-based ARM Mali GPU show that our predictive model outperforms all the convolution operators manually selected by the library.
翻译:深层学习等新兴应用往往是由数据驱动的,因此基于自动拖车的传统方法在实践中所使用的广泛投入中不具有效力。在本文件中,我们开始对基于机器学习技术的预测模型进行调查,以优化革命神经网络。作为一个使用案例,我们侧重于ARM计算图书馆,该图书馆以不同的数字精确度提供三个不同的变动操作器。从整理基准开始,我们建立和验证由决定树和天真的贝耶西亚分类师所学的模型。关于以中观为基础的ARM 马里GPU的初步实验显示,我们的预测模型比图书馆人工挑选的所有变动操作器都好。