Following is our internal news.

  • Undergraduate internship positions available April 05, 2018

    We are recruiting undergraduate research interns.

    Feel free to contact Hyeonuk.

    Details including contact info can be found in the attached poster.

  • Two papers accepted to DAC 2018 February 22, 2018

    The RNC Lab at UNIST has two papers accepted this year to Design Automation Conference (DAC), which will be held in San Francisco, California, in June.

    Congratulations to all the participating authors, Aidyn, Sugil, Hyeonuk, and our undergraduate intern, Saken!

  • NAVER PhD Fellowship Award received by our lab member November 27, 2017

    Congratulations! Hyeonuk was selected for theĀ NAVER Ph.D. Fellowship Award this year.

    NAVER Ph.D. Fellowship Award recognizes top Ph.D. students in UNIST ECE with top-tier conference/journal publications or those who otherwise demonstrated top-quality research results. Only six students received the award this year, and the ceremony took place during the annual ECE Night. Each winner of the award receives five million Korean won, which is about five thousand US dollars.

    Naver Corporation, who is behind this award, is an Internet content service company operating Korea’s #1 search engine according to wikipedia.

    These are some of the photos from the ECE Night.

  • Apply to UNIST graduate school August 31, 2017

    Application schedule for March 2018 admission:

    • Application: 2017.08.22(Tue)-2017.09.07(Thu), 18:00
    • Document submission: 2017.08.22(Tue)-2017.09.08(Fri), 18:00
    • Document screening result notification: 2017.11.16(Thu), 16:00
    • Interview: 2017.11.20(Mon)-12.08(Fri)
    • Final decision notification: 2018.1.10(Wed), 10:00
    • Registration: 2018.01.10(Wed)-01.12(Fri), 16:00

    For detail, please visit (Korean site:, article no. 222.

  • RA (Research Assistant) positions available August 31, 2017

    We have open positions for MS, Ph.D., undergraduate internship, or postdoc in the following areas.

    • Deep learning acceleration based on GPU, FPGA, etc.
    • Cost- and energy-efficient deep learning models
    • Reconfigurable architectures, systems, and compilers
    • Energy-efficient computing based on emerging technology (Memristor, Spintronics, Analog-digital mixed-mode design)
    • Neuromorphic computing, and many more!

    This is a great opportunity. If interested, please write to the admin!

See News Archive for older news.