Electrical Engineering & Computer Science
- 4-102 CST
- [email protected]
- Next-Generation Computing Laboratory
- Energy-Efficient and High-Performance Deep Learning and Artificial Intelligence Systems
- Deep Learning in Embedded, IoT, Precision Medicine, Autonomous Systems, and UAVs
- Cyber-Security in Machine Intelligence and Autonomous Systems; Deep Learning for Secure Cyber-Physical Systems
- Neuromorphic Computing and Non-Von Neumann Computing Paradigms
- Emerging Machine Learning Techniques: Deep Reinforcement Learning, Generative Adversary Networks, Bayesian Neural Networks, etc.
- Security, Privacy, and Acceleration in Genomic Data Analysis
Dr. Wang’s current research interests are the energy-efficient and high-performance implementations of deep learning and artificial intelligence systems, neuromorphic computing and non-von Neumann computing paradigms, cyber-security in deep learning systems, and emerging deep learning algorithms/systems such as Bayesian neural networks, generative adversarial networks (GANs), and deep reinforcement learning. Besides, he works on the application of deep reinforcement learning on mobile and IoT systems, medical systems, and UAVs, with the integration of security protection in deep learning systems. His group works on both algorithms and actual implementations (FPGAs, circuit tapeouts, mobile and embedded systems, and UAVs).
His work has been published in top venues in conferences and journals (e.g. ASPLOS, AAAI, MICRO, ICML, DAC, ICCAD, DATE, ASP-DAC, FPGA, LCTES, INFOCOM, ICDCS, TComputer, TCAD, Plos-One, etc.), and has been cited for around 3,000 times according to Google Scholar. He has received four Best Paper or Top Paper Awards from major conferences including IEEE ICASSP (top 3 among all 2,000+ submissions), ISLPED, IEEE CLOUD, and ISVLSI, with another seven Best Paper Nominations.
- CSE 661/CIS 655: Advanced Computer Architecture
- CIS/CSE 791: Advances in Deep Learning
- CSE/CIS 381: Computer Architecture
- Best Paper Award, IEEE/ACM International Symposium on Low Power Electronic Design (ISLPED), 2014.
- Best Paper Award, IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2014.
- Two journal papers selected as popular papers in IEEE Trans. on Computer-Aided Design, 2014.
- Best Paper Nomination, IEEE Trans. on Computer-Aided Design, 2013.
- Best Paper Nomination, ACM Great Lakes Symposium on VLSI (GLS-VLSI), 2013.
- Top 5 Paper, IEEE Cloud Computing Conference (CLOUD), 2014.
- Ming Hsieh Scholar of USC, 2013.
- Young Student Support Award from Design Automation Conference (DAC), 2011.
- Graduate with Highest Honor in Tsinghua University and Beijing City, 2009.
- Best Undergraduate Thesis Award, Tsinghua University, 2009.
- Best Paper Nomination, International Symposium on Quality Electronic Design (ISQED), 2018.
- Best Paper Nomination, IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC), 2017.
- Best Paper in Track, IEEE International Symposium on Low Power Electronics Design (ISLPED), 2017.
- Three Students (Ao Ren, Ruizhe Cai, Hongjia Li) receive the A Richard Newton Young Student Support Award from Design Automation Conference (DAC), 2017.
- Best Paper Award and Best Student Presentation Award, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017.
- Best Poster Finalist, Numan Research Day, Syracuse University, 2017.
- Best Paper Nomination, IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC), 2015.
Yanzhi Wang, Qing Xie, Ahmed Ammari, and Massoud Pedram, “Deriving a near-optimal power management policy using model-free reinforcement learning and Bayesian classification,” Proc. of Design Automation Conference (DAC), Jun. 2011.
Xue Lin, Yanzhi Wang, and Massoud Pedram, “Joint sizing and adaptive independent gate control for FinFET circuits operating in multiple voltage regimes using logical effort method,” in Proc. of International Conference on Computer-Aided Design (ICCAD), Nov. 2013.
Yanzhi Wang, Yuankun Xue, Alireza Shafaei, Srikanth Ramadurgam, Paul Bogdan, and Massoud Pedram, “A device-circuit-architecture cross-layer framework for prediction of the dark silicon phenomenon using deeply-scaled FinFET devices,” in Dark Silicon Workshop in conjunction with International Conference on Computer-Aided Design (ICCAD), 2014.
Alireza Shafaei, Yanzhi Wang, Xue Lin, and Massoud Pedram, “FinCACTI: Architectural analysis and modeling of caches with deeply-scaled FinFET devices,” in Proc. of IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2014. (Best paper award)
Woojoo Lee, Yanzhi Wang, Donghwa Shin, and Massoud Pedram, “Optimizing a reconfigurable power delivery network for large-area, DVS-enabled OLED displays,” to appear in International Symposium on Low Power Electronics and Design (ISLPED), 2015.
Yanzhi Wang, Xue Lin, and Massoud Pedram, “A Stackelberg game-based optimization framework of the smart grid with distributed PV power generations and data centers,” IEEE Trans on Energy Conversion, 2015.