Li, Hai

Vortex: variation-aware training for memristor X-bar

Liu, B., Li, H., Chen, Y., Li, X., Wu, Q., & Huang, T. (2015) · Proceedings of the 52nd Annual Design Automation Conference

Advanced Memory and Neural ComputingNeural Networks and Applications

Cited by 197

FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking

Li, A., Sun, J., Zeng, X., Zhang, M., Li, H., & Chen, Y. (2021) · Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 139

MeDNN: A distributed mobile system with enhanced partition and deployment for large-scale DNNs

Mao, J., Yang, Z., Wen, W., Wu, C., Song, L., Nixon, K. W., Chen, X., Li, H., & Chen, Y. (2017) · 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 97

A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models

Guo, C., Cheng, F., Du, Z., Kiessling, J., Ku, J., Li, S., Li, Z., Ma, M., Molom-Ochir, T., Morris, B., et al. (2025) · IEEE Circuits and Systems Magazine

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 32

FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices

Tang, M., Zhang, J., Ma, M., DiValentin, L., Ding, A., Hassanzadeh, A., Li, H., & Chen, Y. (2022) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 0