Research
Research Area
- Embedded Artificial Intelligence (On-Device AI & On-Device Machine Learning)
Efficient Deep Learning Inference, Training, and Adaptation
Embedded Computer Vision, NLP (Natural Language Processing), Reinforcement Learning, Multitask Learning, and Meta Learning
On-Device Interpretable and Explainable AI (XAI)
On-Device Neural Architecture Search (On-Device NAS)
Generative AI on Embedded Systems (Image/Video Generation and Language Models)
On-Device Self-Supervised Learning and Continual Learning
Online Dataset Adaptation and Neural Architecture Search
Real-time Machine Learning
On-Device Neural Architecture Search (SenSys 2023)
Softmax Output Approximation (NeurIPS 2023)
MicroDeblur (IPSN 2023)
Best Paper Runner-Up Award
Neural Weight Virtualization (MobiSys 2020)
Weight Separation (PerCom 2022)
Deep Functional Network (IPSN 2021)
- Embedded, Real-Time, and Sensing Systems
Real-time Inference and Training of Machine Learning Algorithms
Intelligent Robot Systems
Smart Sensing Systems
SubFlow (RTAS 2020)
- Collaborative, Collective, and Federated Learning
Multi-Device, Multi-Sensing, and Multi-Modal LearningÂ
Distributed Learning
Intermittent (Battery-less) Computing
On-demand Federated Learning (AISTATS 2024)
Intermittent Learning (IMWUT 2019/UbiComp 2020)
Best Presentation Award
- AIoT (AI + IoT) and Intelligent Edge
Connected and Networked Intelligence
Learning in the wild (AIChallengeIoT 2020)
Best Paper Award
- Mobile Computing and Smart Sensing Systems
AI and Machine Learning on Microcontrollers with Extreme Resource Constraints
NeuroZero (SenSys 2019)
- Cyber-Physical Systems
Deep Controller Theory
Knowledge Transfer Between Embedded Controllers (DCOSS 2018)
- Neural Memory
Next-generation Neural Memory Architectures and Algorithms
Neural Memory for Machine Reasoning and Artificial General Intelligence
- Deep Learning Compilers
Generation and Optimization of Efficient Program Code and Executable Binary for Deep Learning Models
- Confidential On-Device Deep Learning and Machine Learning
Enabling secure and confidential deep learning (machine learning) on resource-constrained embedded/mobile/IoT devices
Sponsors
We are very grateful for the support and assistance with our research and projects.
National Research Foundation
Agency for Defense Development
Electronics and Telecommunications Research Institute
Korea Technology and Information Promotion Agency for SMEs
Ulsan National Institute of Science and Technology
Hyundai Motor Company
Samsung Electronics