论文标题
Resperfnet：深层神经网络回归性能建模的深层学习
ResPerfNet: Deep Residual Learning for Regressional Performance Modeling of Deep Neural Networks
论文作者
论文摘要
计算技术的快速进步促进了各种深度学习应用的发展。不幸的是，并行计算基础架构的效率因神经网络模型而差异很大，这阻碍了设计空间的探索，以在给定应用程序的特定计算平台上找到高性能的神经网络体系结构。为了应对这一挑战，我们提出了一种基于深度学习的方法Resperfnet，该方法通过在目标平台上获得的代表性数据集训练残留的神经网络，以预测深神经网络的性能。我们的实验结果表明，Resperfnet可以准确预测各种平台上单个神经网络层和完整网络模型的执行时间。特别是，Resperfnet在NVIDIA GTX 1080TI上达到了LENET，ALEXNET和VGG16的平均绝对百分比误差的8.4％，这大大低于先前发表的作品。
The rapid advancements of computing technology facilitate the development of diverse deep learning applications. Unfortunately, the efficiency of parallel computing infrastructures varies widely with neural network models, which hinders the exploration of the design space to find high-performance neural network architectures on specific computing platforms for a given application. To address such a challenge, we propose a deep learning-based method, ResPerfNet, which trains a residual neural network with representative datasets obtained on the target platform to predict the performance for a deep neural network. Our experimental results show that ResPerfNet can accurately predict the execution time of individual neural network layers and full network models on a variety of platforms. In particular, ResPerfNet achieves 8.4% of mean absolute percentage error for LeNet, AlexNet and VGG16 on the NVIDIA GTX 1080Ti, which is substantially lower than the previously published works.
