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