Towards Pervasive and User Satisfactory CNN across GPU Microarchitectures

@article{Song2017TowardsPA,
  title={Towards Pervasive and User Satisfactory CNN across GPU Microarchitectures},
  author={Mingcong Song and Yang Hu and Huixiang Chen and Tao Li},
  journal={2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)},
  year={2017},
  pages={1-12}
}
Accelerating Convolutional Neural Networks (CNNs) on GPUs usually involves two stages: training and inference. Traditionally, this two-stage process is deployed on high-end GPU-equipped servers. Driven by the increase in compute power of desktop and mobile GPUs, there is growing interest in performing inference on various kinds of platforms. In contrast to the requirements of high throughput and accuracy during the training stage, end-users will face diverse requirements related to inference… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

BenchIP: Benchmarking Intelligence Processors

Journal of Computer Science and Technology • 2018
View 1 Excerpt

In-Situ AI: Towards Autonomous and Incremental Deep Learning for IoT Systems

2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) • 2018
View 1 Excerpt

Prediction of SGEMM GPU Kernel Performance Using Supervised and Unsupervised Machine Learning Techniques

2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) • 2018
View 1 Excerpt

AEP: An error-bearing neural network accelerator for energy efficiency and model protection

2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) • 2017
View 1 Excerpt

Similar Papers

Loading similar papers…