This is a partial list of the publications by the Stanford Concurrent VLSI Architecture group organized by project. Click on the Section of the page you would like to browse.
EIE: Efficient Inference Engine on Compressed Deep Neural Network
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark Horowitz, William J. Dally
International Symposium on Computer Architecture (ISCA), June 2016; Hotchips, Aug 2016.
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han, Huizi Mao, William J. Dally
NIPS Deep Learning Symposium, December 2015.
International Conference on Learning Representations (ICLR), May 2016, Best Paper Award.
Learning both Weights and Connections for Efficient Neural Networks
Song Han, Jeff Pool, John Tran, William J. Dally
Advances in Neural Information Processing Systems (NIPS), December 2015.
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Shijian Tang, Erich Elsen, Bryan Catanzaro, John Tran, William J. Dally
International Conference on Learning Representations (ICLR), April 2017.
SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and < 0.5MB Model Size
Forrest Iandola, Song Han, Matthew Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
arXiv 2016.