Yanzhi Wang

Yanzhi Wang

Assistant Professor

Electrical Engineering & Computer Science


  • Ph.D.

Lab/Center Affiliations:

  • Next-Generation Computing Laboratory
  • CASE

Research Interests:

  • High-Performance and Energy-Efficient Computing
  • Extremely Low-Power Near-Threshold Computing for Next-Generation Devices
  • Neuromorphic Computing Systems for Hardware Acceleration and Cognitive Frameworks
  • Non-Volatile Computing Systems for Embedded Wearable Devices

Current Research:

Yanzhi Wang’s major research interests are on next-generation computing paradigms in the big data era, including (i) near-threshold computing for next-generation devices that achieves the optimal tradeoff among energy consumption, performance, and reliability, (ii) neuromorphic computing systems for hardware acceleration and cognitive frameworks, and (iii) non-volatile computing systems for ambient energy-harvesting wearable devices. His research focuses on both high-performance computing systems and low-power embedded wearable devices.

Courses Taught:

  • CSE/ELE 664: VLSI Design Methods


  • 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.

Selected Publications:

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.