Execution Time Modeling for CNN Inference on Embedded GPUs
暂无分享,去创建一个
[1] Hamed Taherdoost,et al. Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research , 2016 .
[2] Matti Siekkinen,et al. Latency and throughput characterization of convolutional neural networks for mobile computer vision , 2018, MMSys.
[3] Thomas F. La Porta,et al. Augur: Modeling the Resource Requirements of ConvNets on Mobile Devices , 2021, IEEE Transactions on Mobile Computing.
[4] BengioYoshua,et al. Random search for hyper-parameter optimization , 2012 .
[5] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[6] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[7] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Denis Trystram,et al. A comparison of GPU execution time prediction using machine learning and analytical modeling , 2016, 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA).
[9] Shuaiwen Song,et al. A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures , 2013, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.
[10] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[11] Paolo Napoletano,et al. Benchmark Analysis of Representative Deep Neural Network Architectures , 2018, IEEE Access.
[12] A. Stephen McGough,et al. Predicting the Computational Cost of Deep Learning Models , 2018, 2018 IEEE International Conference on Big Data (Big Data).
[13] Holger Fröning,et al. A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels , 2021, ACM Trans. Archit. Code Optim..
[14] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[15] Ameet Talwalkar,et al. Paleo: A Performance Model for Deep Neural Networks , 2016, ICLR.
[16] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[18] Eugenio Gianniti,et al. Performance Prediction of GPU-Based Deep Learning Applications , 2018, 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD).