论文标题
建立基于学习的绩效建模，以加速深层神经网络
Towards a learning-based performance modeling for accelerating Deep Neural Networks
论文作者
论文摘要
诸如深度学习之类的新兴应用程序通常是数据驱动的，因此基于自动调节器的传统方法在实践中使用的各种输入中均无效。在本文中，我们开始研究基于机器学习技术的预测模型，以优化卷积神经网络（CNN）。作为用例，我们专注于ARM计算库，该库以不同的数字精度提供了三种不同的卷积操作员实现。从基准的整理开始，我们构建了由决策树和天真贝叶斯分类器学到的模型。基于Midgard的ARM MALI GPU的初步实验表明，我们的预测模型优于图书馆手动选择的所有卷积操作员。
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.
