I am Jiecao YU, a PhD student in the University of Michigan.
This is a paper reading list about DNN acceleration (also general Deep Learning).
Still under construction.
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications [ paper | 20170612001 ]
- LightRNN: Memory and Computation-Efficient Recurrent Neural Networks [ paper | 20170612005 ]
- ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices [ paper | 20170713001 ]
- Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators [ paper | 20170630001 ]
- Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning [ paper | 20170612002 ]
- Learning Structured Sparsity in Deep Neural Networks [ paper | 20170612003 ]
- Dynamic Network Surgery for Efficient DNNs [ paper | 20170612004 ]
- Pruning Filters for Efficient ConvNets [ paper | 20170901001 ]
- Data-Driven Sparse Structure Selection for Deep Neural Networks [ paper | 20170901002 ]
- ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression [ paper | 20170901003 ]
- Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism [ paper | 20170901004 ]
Binarized neural network
- Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 [ paper | 20170901005 ]
- XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks [ paper | 20170901006 ]