Performance Prediction for Convolutional Neural Networks in Edge Devices

F. Murtagh, “Multilayer perceptrons for classification and regression,” Neurocomputing , vol. 2, no. 5-6, pp. 183–197, 1991

1991

Earlier work this paper cites.

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intelligent Systems and their applications , vol. 13, no. 4, pp. 18–28, 1998

1998

Earlier work this paper cites.

J. D. Rodriguez, A. Perez, and J. A. Lozano, “Sensitivity analysis of k-fold cross validation in prediction error estimation,” IEEE transactions on pattern analysis and machine intelligence , vol. 32, no. 3, pp. 569–575, 2009

2009

Earlier work this paper cites.

E. Bisong, “Google colaboratory,” in Building Machine Learning and Deep Learning Models on Google Cloud Platform . Springer, 2019, pp. 59–64

Original

2010

Earlier work this paper cites.

M. Amarís, R. Y. de Camargo, M. Dyab, A. Goldman, and D. Trystram, “A comparison of gpu execution time prediction using machine learning and analytical modeling,” in 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA) , 2016, pp. 326–333

2016

Earlier work this paper cites.

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining , 2016, pp. 785–794

2016

Earlier work this paper cites.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al. , “Tensorflow: A system for large-scale machine learning,” in 12th USENIX symposium on operating systems design and implementation OSDI 16) , 2016, pp. 265–283

2016

Earlier work this paper cites.

H. Qi, E. R. Sparks, and A. Talwalkar, “Paleo: A performance model for deep neural networks,” in ICLR , 2017

2017

Cited alongside, same era.

C. F. Rodrigues, G. Riley, and M. Luján, “Fine-grained energy profiling for deep convolutional neural networks on the jetson TX1, year=2017,” in 2017 IEEE International Symposium on Workload Characterization (IISWC)

2017

Cited alongside, same era.

E. Cai, D.-C. Juan, D. Stamoulis, and D. Marculescu, “Neuralpower: Predict and deploy energy-efficient convolutional neural networks,” in Proceedings of The 9th Asian Conference on Machine Learning, ACML , 2017

2017

Cited alongside, same era.

N. Matloff, Statistical regression and classification: from linear models to machine learning . CRC Press, 2017

2017

Cited alongside, same era.

S. Bianco, R. Cadene, L. Celona, and P. Napoletano, “Benchmark analysis of representative deep neural network architectures,” IEEE Access , vol. 6, pp. 64 270–64 277, 2018

“Jetson AGX xavier developer kit,” accessed: 2018-06-30. [Online]. Available: developer.nvidia.com/embedded/jetson-agx-xavier-developer-kit

2018

Later among the works it cites.

“Harness AI at the edge with the jetson TX2 developer kit,” accessed: 2018-06-30. [Online]. Available: developer.nvidia.com/embedded/jetson-tx2-developer-kit

2018

Later among the works it cites.

K. Siu, D. M. Stuart, M. Mahmoud, and A. Moshovos, “Memory requirements for convolutional neural network hardware accelerators,” in 2018 IEEE International Symposium on Workload Characterization (IISWC) , 2018, pp. 111–121

2018

Later among the works it cites.

J. Chen and X. Ran, “Deep learning with edge computing: A review,” Proceedings of the IEEE , vol. 107, no. 8, pp. 1655–1674, 2019

2019

Later among the works it cites.