Home · Tao Chen

About Me

I am a software engineer at Google working on infrastructure for cloud computing and machine learning.

I obtained my Ph.D. in Electrical and Computer Engineering from Cornell University in 2018. I worked on Computer Architecture and Heterogeneous Computing with Prof. G. Edward Suh in the Computer Systems Laboratory. Before joining Cornell, I received my B.S. in Microelectronics from Fudan University, Shanghai, China, in 2012.

CV available upon request.

Research Interests

My research interests are in computer architecture, heterogeneous computing, and domain-specific architectures.

Publications

[Google Scholar]

Sheng Li, Garrett Andersen, Tao Chen, Liqun Cheng, Julian Grady, Da Huang, Quoc V Le, Andrew Li, Xin Li, Yang Li, Chen Liang, Yifeng Lu, Yun Ni, Ruoming Pang, Mingxing Tan, Martin Wicke, Gang Wu, Shengqi Zhu, Parthasarathy Ranganathan, Norman P Jouppi
Hyperscale Hardware Optimized Neural Architecture Search, Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), March 2023. [ACM] [PDF] [Abstract]
Tao Chen, Shreesha Srinath, Christopher Batten, and G. Edward Suh
An Architectural Framework for Accelerating Dynamic Parallel Algorithms on Reconfigurable Hardware, Proceedings of the 51st IEEE/ACM International Symposium on Microarchitecture (MICRO), October 2018. [IEEE] [PDF] [Slides] [Abstract]
Tao Chen
Architectural Frameworks for Automated Design and Optimization of Hardware Accelerators, Ph.D. Dissertation, Cornell University, May 2018. [PDF]
Tao Chen and G. Edward Suh
Efficient Data Supply for Hardware Accelerators with Prefetching and Access/Execute Decoupling, Proceedings of the 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), October 2016. [IEEE] [PDF] [Slides] [Abstract]
Tao Chen, Alex Rucker, and G. Edward Suh
Execution Time Prediction for Energy-Efficient Hardware Accelerators, Proceedings of the 48th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), December 2015. [ACM] [PDF] [Slides] [Abstract]
Daniel Lo, Tao Chen, Mohamed Ismail, and G. Edward Suh
Run-Time Monitoring with Adjustable Overheads Using Dataflow-Guided Filtering, Proceedings of the 21st IEEE International Symposium on High Performance Computer Architecture (HPCA), February 2015. [IEEE] [PDF] [Abstract]
Daniel Lo, Mohamed Ismail, Tao Chen, and G. Edward Suh
Slack-Aware Opportunistic Monitoring for Real-Time Systems, Proceedings of the 20th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), April 2014. [IEEE] [PDF] [Abstract]
Tao Chen, Jiawei Zheng, Xingsi Zhang, Shengchang Cai, and Yun Chen
A Hardware Accelerator for Speech Recognition Applications, Proceedings of the 9th IEEE International Conference on ASIC (ASICON), October 2011. [IEEE] [Abstract]

Teaching Experience

Teaching Assistant for ENGRD/ECE 2300 Digital Logic and Computer Organization, Fall 2015

Industry Experience

Software Engineer, Google, June 2018 –
Hardware Engineering Intern, Google, May 2016 – Aug 2016

ACM and IEEE-mandated Copyright Notice

The documents listed above are posted as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Tao Chen (陈涛)