REASEARCH
Noh, Dong Kun
Distributed Computing involves multiple entities talking to one another in some way, whilte also performing their own operations. DCLab is conducting research related to “Intelligent Distributed Computing” such as intelligent IoT, green USN, and edge AI. If you are interested in our lab, please feel free to contact Prof. Noh (dnoh@ssu.ac.kr).
http://dclab.ssu.ac.krLee, Eunji
DATOS(Data Analysis To Computer Systems) Laboratory was established in 2014 by Prof. Eunji Lee and has been conducting research on cutting-edge software solutions for data management systems. We are currently interested in unstructured data maintenance including key-value store, object store, and I/O subsystem for next-generation applications (AI, IoT, Blockchain) and emerging memory technologies (Persistent Memory, Flash Storage). If you are interested in working with us on the areas of memory and storage systems, please email me with your resume (ejlee@ssu.ac.kr).
https://sites.google.com/view/datoslabShin, Donghwa
The advance of modern computing systems is limited by the power and thermal problems that result from rapid increases in device densities and operating speeds. The trend towards mobile computing with battery-driven devices naturally puts a premium on low-power systems; and fixed systems are also candidates for this technology, because of the move from an electricity grid based on continuous generation from fossil fuel to distributed generation reliant on fluctuating environmental energy sources. The concept of energy-efficient system design is thus essential to the future direction of computing systems. Energy-aware computing lab. (ECLab) conducts research on the energy-efficient computing systems design in at various levels, including RTL, PCB, OS, and application software. Current research topics includes i) Hardware security with memristor devices, ii) Processing-in- memory techniques for computation acceleration for neural network applications, iii) Efficiency memory management for reconfigurable neuromorphic computing systems, iv) design automation method for quantum computing v) an integration of PCM into the DRAM-compatible system architecture considering efficiency and performance. Recently we are especially focusing on the accelerator and memory architecture for neuromorphic computing. Since modern neural network-based applications show much different behavior than the conventional applications, we should devise a new way of management of computation and memory. It is highly related to the competitiveness of the students and industry in the near future.
https://scholar.google.com/citations?user=fb2BnP0AAAAJ&hl=enChung, Kyusik
http://nclab.ssu.ac.krHan, Youngjoon
http://visionlab.ssu.ac.krKim, Kanghee
http://rubicom.ssu.ac.kr