  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.
