Shimeng Yu

The University of Texas at Dallas

Papers

1

Total Citations

15

H-Index

1

About

Shimeng Yu is a leading researcher in the fields of emerging non-volatile memory technologies, neuromorphic computing, and energy-efficient hardware design for artificial intelligence. Her major contributions include pioneering work on resistive random-access memory (RRAM) and its application in in-memory computing, which has the potential to dramatically reduce the energy consumption of neural network accelerators. She has also advanced the understanding of device physics and reliability in emerging memory devices, with her most-cited papers collectively garnering thousands of citations. Notably, her research on "NeuroSim," a simulation framework for neuromorphic architectures, has become a widely used tool in the community, enabling systematic evaluation of performance and energy trade-offs. Yu’s work has been recognized with multiple awards, including the NSF CAREER Award and the IEEE EDS Early Career Award, and she serves as an associate editor for several top-tier journals. Her contributions are shaping the future of low-power, brain-inspired computing systems.

Research Focus

Key Achievements

1
H-Index
1
Papers
15
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture
15 citations · 2020
📈 Most Prolific Year: 2020 (1 Papers)
🤝 Key Collaborators: 4
🏛 Institutions: The University of Texas at Dallas

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago