NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Le Cun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: Advances in Neural Information Processing Systems (1990)

1990

Earlier work this paper cites.

Audet, C., J. E. Dennis, J.: A progressive barrier for derivative-free nonlinear programming. SIAM Journal on Optimization 20

2009

Earlier work this paper cites.

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 248–255. IEEE (2009)

2009

Earlier work this paper cites.

Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. In: International Conference on Learning Representations (ICLR) (2014)

2014

Earlier work this paper cites.

Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems. pp. 1135–1143 (2015)

2015

Earlier work this paper cites.

Kim, Y.D., Park, E., Yoo, S., Choi, T., Yang, L., Shin, D.: Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530 (2015)

Original

2015

Earlier work this paper cites.

Srinivas, S., Babu, R.V.: Data-free parameter pruning for deep neural networks. arXiv preprint arXiv:1507.06149 (2015)

Original

2015

Earlier work this paper cites.

Yang, Z., Moczulski, M., Denil, M., de Freitas, N., Smola, A., Song, L., Wang, Z.: Deep fried convnets. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1476–1483 (2015)

2015

Earlier work this paper cites.

Chen, Y.H., Emer, J., Sze, V.: Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks. In: Proceedings of the 43rd Annual International Symposium on Computer Architecture (ISCA) (2016)

2016

Earlier work this paper cites.

Chen, Y.H., Krishna, T., Emer, J., Sze, V.: Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks. IEEE Journal of Solid-State Circuits 52

2016

Cited alongside, same era.

He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

2016

Cited alongside, same era.

Hu, H., Peng, R., Tai, Y.W., Tang, C.K.: Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures. arXiv preprint arXiv:1607.03250 (2016)

Original

2016

Cited alongside, same era.

Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: Advances in Neural Information Processing Systems. pp. 4107–4115 (2016)

2016

Cited alongside, same era.

Molchanov, P., Tyree, S., Karras, T., Aila, T., Kautz, J.: Pruning convolutional neural networks for resource efficient transfer learning. arXiv preprint arXiv:1611.06440 (2016)

Yang, Tien-Ju and Chen, Yu-Hsin and Emer, Joel and Sze, Vivienne: A Method to Estimate the Energy Consumption of Deep Neural Networks. In: Asilomar Conference on Signals, Systems and Computers (2017)

2017

Later among the works it cites.

Yang, Tien-Ju and Chen, Yu-Hsin and Sze, Vivienne: Designing energy-efficient convolutional neural networks using energy-aware pruning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

2017

Later among the works it cites.

Yu, J., Lukefahr, A., Palframan, D., Dasika, G., Das, R., Mahlke, S.: Scalpel: Customizing dnn pruning to the underlying hardware parallelism. In: Proceedings of the 44th Annual International Symposium on Computer Architecture (2017)

2017

Later among the works it cites.

Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:1707.01083 (2017)

Original

2017

Later among the works it cites.