Postdoctoral Researcher, Seoul National University · Advisor: Prof. Jae W. Lee
I design acceleration techniques for the fundamental operations behind modern deep learning — co-designing hardware and software, from RTL and the Linux kernel up to frameworks and model code, so that critical applications run faster and scale further.
I develop novel acceleration techniques for fundamental operations, primarily through hardware–software co-design, paving the way for more efficient and scalable adoption of critical applications.
Accelerating Machine Learning Interatomic Potentials by targeting the tensor product — the core and most expensive operation in equivariant neural networks — through custom GPU kernel design.
Designing a dedicated hardware accelerator with a fused kernel for scalable inference of state space models, a promising architecture for efficient long-sequence modeling.
Earlier, accelerating a broad range of workloads — LLMs, CNNs, inverted-index search, and mobile application switching — with full-stack expertise from hardware design and the Linux kernel to software-stack and DNN-model optimization.
Open to research roles.
Reach me at triomphant1@snu.ac.kr