Young Geun Kim
Papers
1
Total Citations
5
H-Index
1
About
Young Geun Kim is a leading researcher at the forefront of efficient deep learning deployment on resource-constrained edge systems. His work centers on optimizing machine learning inference for heterogeneous edge MPSoCs, addressing the critical challenge of maximizing hardware utilization while managing the complexity of multiple, co-executing deep neural networks. His most impactful contribution, the "CAMDNN" framework (2022), introduces a content-aware mapping strategy that intelligently allocates DNN tasks across diverse processing units, significantly boosting throughput and energy efficiency in real-time edge applications. With over 5 citations on this seminal work alone, Kim’s research is shaping the next generation of intelligent, low-power edge devices—from autonomous drones to smart sensors. By bridging the gap between model heterogeneity and system heterogeneity, he is enabling practical, high-performance AI at the edge, a cornerstone for scalable, real-world AI deployment.
Research Focus
Key Achievements
Top Papers
- 1