Efficient Machine Learning

for Internet of things and edge computing ecosystems

Many applications in the Internet of Things (IoT) and edge computing extract useful information and learn patterns from massive data streams. It poses substantial technical challenges due to the slow response time, scalability issue of learning solutions, and limited resources.

We focus on how to redesign learning by closely modeling the ultimate efficient processor - the human brain. We also explore the potential of learning solutions for future emerging and alternative system designs.


Brain-inspired Hyperdimensional Computing

Congnitive Learning Based on Human Memory Model

Hyperdimensional (HD) computing is an alternative computing method, which processes cognitive tasks in a light-weight and error-torrent way, based on theoretical neuroscience. We work on developing various learning tasks such as classification, regression, clustering, and reinforcement learning using HD computing.


Edge Computing

Learning on Low-power Edge Devices

We build state-of-the-art learning software and hardware on low-power devices. It includes designing self-learning systems capable of autonomous sensing, learning, and actuating on diverse IoT platforms.


Systems for ML and
ML for Systems

Alternative Computing

We rethink the role of machine learning (ML) for systems. We explore diverse alternative system solutions such as in-memory computing, near-data computing, and ML-driven system software.



We collaborate with worldwide industries and top universities, including SK Hynix, UC San Diego, UC Irvine, ETH Zurich, and Seoul National University. We look forward to other potential collaborations!


  • 2021 Feb   A paper written by Jiseung Kim and Yeseong Kim is accepted in DAC 2021.
  • 2021 Feb   A paper presented in HPCA 2021. Three papers presented in DATE 2021.
  • 2021 Feb   New grad/undergrad students joined!
  • 2020 Jun   Prof. Yeseong Kim joined at DGIST and opened the CELL lab.


E3 Building Room 613, 333, Techno jungang-daero, Hyeonpung-myeon Dalseong-gun, Daegu, 42988, REPUBLIC OF KOREA




(+82) 053-785-6332