Invited Talks

Bandwidth-Efficient Deep Learning on Edge Devices
- Sony, San Jose, November 2017
- Renesas, Santa Clara, November 2017
- Qualcomm, San Diego, December 2017
- CloudMinds, Santa Clara, December 2017
- Samsung AI Summit, Mountain View, January 2017

Efficient Methods and Hardware for Deep Learning
- Faculty interview, MIT, Princeton, UC Berkely, UT Austin, etc., March 2017
- PhD thesis defense, Stanford, June 2017
- Invited talk, Tsinghua University, Peking University, Shanghai Jiaotong University, Fudan University, Zhejiang University, University of Science and Technology of China, China Academy of Science, September 2017

Deep Learning – Tutorial and Recent Trends
- Conference tutorial at FPGA’17, Monterey. [slides]

Deep Compression: A Deep Neural Network Compression Pipeline
- Conference talk at ICLR, Puerto Rico, May 2016.
- GPU Technology Conference (GTC), San Jose, March 2016.
- Google, Mountain View, March 2015.
- Stanford Computer System Colloquium, January 2016.
- Baidu, Beijing, December 2015.
- Huawei, Shanghai, December 2015.
- Horizon Robotics, Beijing, December 2015.

EIE: Efficient Inference Engine on Compressed Deep Neural Network
- Conference talk at ISCA, Korea, June 2016.
- Movidius, San Mateo, April 2016.
- HP Labs, Palo Alto, February 2016.
- Apple, Cupertino, December 2015.
- Huawei, Shanghai, December 2015.
- HiScene, Shanghai, December 2015.
- Stanford SystemX Fall Conference, Stanford, November 2015.

Techniques for Efficient Implementation of Deep Neural Networks
- Embedded Vision Alliance Member Meeting, March 2016.

Deep Compression, DSD Training and EIE: Deep Neural Network Model Compression, Regularization and Hardware Acceleration
- O’Reilly Artificial Intelligence Conference, New York, Sep 2016.
- Facebook, Menlo Park, Aug 2016.
- Tesla, Palo Alto, Aug 2016.
- Xilinx, Santa Clara, Aug 2016.
- OpenAI, San Francisco, Aug 2016. Video.
- Microsoft Research Asia, Beijing, June 2016.
- Microsoft Research, Redmond, June 2016.
- Apple, Cupertino, June 2016.