Accepted Papers

Best Poster

• A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices. Xiaofan Zhang, Hao Cong, Yuhong Li, Yao Chen, Jinjun Xiong, Wen-Mei Hwu and Deming Chen

Best Paper

• DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression. Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marban Gonzalez, Talmaj Marinc, Heiko Schwarz, Detlev Marpe, Thomas Wiegand, Ahmed Osman and Wojciech Samek
• Single-Path NAS: Device-Aware Efficient ConvNet Design. Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu and Diana Marculescu

List of all accepted papers

• Xiaofan Zhang, Hao Cong, Yuhong Li, Yao Chen, Jinjun Xiong, Wen-Mei Hwu and Deming Chen. A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices
  • https://arxiv.org/abs/1905.08369
• Kushal Datta, Aishwarya Bhandare, Deepthi Karkada, Vamsi Sripathi, Sun Choi, Vikram Saletore and Vivek Menon. Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model
  • http://arxiv.org/abs/1906.00532
• Bin Yang, Lin Yang, Xiaochun Li, Wenhan Zhang, Hua Zhou, Yequn Zhang, Yongxiong Ren and Yinbo Shi. 2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for image retrieval
  • https://arxiv.org/abs/1905.03362
• Zhong Qiu Lin, Brendan Chwyl and Alexander Wong. EdgeSegNet: A Compact Network for Semantic Segmentation
  • https://arxiv.org/abs/1905.04222
• Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marban Gonzalez, Talmaj Marinc, Heiko Schwarz, Detlev Marpe, Thomas Wiegand, Ahmed Osman and Wojciech Samek. DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
  • http://arxiv.org/abs/1905.08318
• Trevor Gale, Erich Elsen and Sara Hooker. The State of Sparsity in Deep Neural Networks
  • https://arxiv.org/abs/1902.09574 (to be updated)
• Sheng Lin, Xiaolong Ma, Shaokai Ye, Geng Yuan, Kaisheng Ma and Yanzhi Wang. Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM
  • https://arxiv.org/abs/1905.00789
• Chengcheng Li, Zi Wang, Dali Wang, Xiangyang Wang and Hairong Qi. Investigating Channel Pruning through Structural Redundancy Reduction - A Statistical Study
  • https://arxiv.org/abs/1905.06498
• Kartikeya Bhardwaj, Naveen Suda and Radu Marculescu. Dream Distillation: A Data-Independent Model Compression Framework
  • http://arxiv.org/abs/1905.07072
• Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu and Diana Marculescu. Single-Path NAS: Device-Aware Efficient ConvNet Design
  • https://arxiv.org/abs/1905.04159
• Wei Niu, Yanzhi Wang and Bin Ren. CADNN: Ultra Fast Execution of DNNs on Mobile Devices with Advanced Model Compression and Architecture-Aware Optimization
  • https://arxiv.org/abs/1905.00571
• Jonathan Ephrath, Lars Ruthotto, Eldad Haber and Eran Treister. LeanResNet: A Low-cost yet Effective Convolutional Residual Networks
  • https://arxiv.org/abs/1904.06952
• Dushyant Mehta, Kwang In Kim and Christian Theobalt. Implicit Filter Sparsification In Convolutional Neural Networks
  • https://arxiv.org/abs/1905.04967
• Yushu Feng, Huan Wang and Haoji Hu. Triplet Distillation for Deep Face Recognition
  • https://arxiv.org/abs/1905.04457
• Mohamadali Torkamani, Phillip Wallis, Shiv Shankar and Amirmohammad Rooshenas. Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions
  • https://arxiv.org/abs/1905.07685