- High-performance system design for training deep neural networks
- Lightweight machine learning algorithms for energy-efficient inference engines
- Automated search of lightweight deep learning algorithms
- Energy-efficient hardware accelerator for scientific computing
- Understanding biological neurons by improving the performance of spike-based neural nets
- Brain signal reconstruction using artificial intelligence and its hardware implementation
- Power-aware/thermal-aware system design methodology
Broader research interests include VLSI design, digital IC design, accelerator/computer architecture.
Please contact Prof. Kung if interested.