Dynamic Runtime Feature Map Pruning

24 December 2018

Papers citing "Dynamic Runtime Feature Map Pruning"

7 / 7 papers shown

Title
Mixed Precision Training of Convolutional Neural Networks using Integer Operations Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj D. Kalamkar Sasikanth Avancha ... J. Corbal N. Shustrov R. Dubtsov Evarist Fomenko V. Pirogov MQ 34 154 0 03 Feb 2018
Dynamic Routing Between Capsules S. Sabour Nicholas Frosst Geoffrey E. Hinton 60 4,584 0 26 Oct 2017
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks Minsoo Rhu Mike O'Connor Niladrish Chatterjee Jeff Pool S. Keckler 40 176 0 03 May 2017
Efficient Processing of Deep Neural Networks: A Tutorial and Survey Vivienne Sze Yu-hsin Chen Tien-Ju Yang J. Emer AAML 3DV 83 3,002 0 27 Mar 2017
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size F. Iandola Song Han Matthew W. Moskewicz Khalid Ashraf W. Dally Kurt Keutzer 100 7,448 0 24 Feb 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 168 8,793 0 01 Oct 2015
Caffe: Convolutional Architecture for Fast Feature Embedding Yangqing Jia Evan Shelhamer Jeff Donahue Sergey Karayev Jonathan Long Ross B. Girshick S. Guadarrama Trevor Darrell VLM BDL 3DV 139 14,703 0 20 Jun 2014