Research
Our research area includes system optimization for machine learning, carbon footprint-aware computing, energy/temperature management for mobile/edge systems, domain-specific accelerator design (e.g, deep learning), etc. These research topics are being actively studied in the industry as well as academia.
Research interests include various topics in Computer Systems, Machine Learning, Artificial Intelligence, etc.
System Design and Optimization for Machine Learning
Carbon Footprint-aware Computing Towards Sustainable Deep Learning
Energy Efficiency Optimization of Mobile/Edge Systems
Thermal Management of Multi-scale Heterogeneous Systems
Domain-specific (e.g., AI/ML) H/W Design and Optimization
System Optimization for Machine Learning
Energy Efficient Edge Inference
Federated Learning
Edge Recommendation
End-to-End AI Pipeline
Carbon Footprint-aware Computing
Carbon Footprint-aware Deep Learning
Energy/Latency/Carbon Footprint Trade-offs
Use of Renewable Energy
Domain-specific Accelerator Design
Processing-in-Memory (PIM) Architecture
Deep Learning/AI Accelerator
3D Logic-Memory Cube