Faculty

The research area of ICCL is algorithm/hardware/software co-design, which is crucial for application-specific accelerators and system-on- chip (SoC) optimization. We have students from both computer science and electrical engineering. Research topics of ICCL include (i) architecture and tools for emerging technologies such as AI/ML, Processing-in-Memory (PIM), and cryptography, and (ii) embedded machine learning such as DNN quantization. ICCL is very active in the area of system-level semiconductor design automation, with strong academic presence, industrial collaboration, and international networking.

[Curriculum Vitae]

● 2009-Present: Professor (since 2020), Department of Electrical Engineering, UNIST
● 2017-2018: Visiting professor at University of Toronto
● 2007-2009: Post-doc researcher at Arizona State University
● 2004-2007: Senior researcher at Samsung Electronics
● 2002-2003: Visiting scholar at University of California-Irvine

[Education]

● 2004: Ph.D. in Electrical Engineering and Computer Science, Seoul National University
● 1999: M.Sc. in Electrical Engineering, Seoul National University
● 1997: B.Sc. in Electrical Engineering, Seoul National University

[Research  Keywords and Topics]

Application-Specific Accelerator Design,
Architecture and Tools for Emerging Technology
(AI/ML, Processing-in-Memory, Cryptography),
DNN Quantization,
Architecture and Tools for High-Performance Computing,
Coarse-Grained Reconfigurable Architecture, Neuromorphic Systems

[Publications (selected)]

● “Non-Uniform Step Size Quantization for Accurate Post-Training Quantization,” Sangyun Oh, Hyeonuk Sim, Jounghyun Kim and Jongeun Lee, Proc. of European Conference on Computer Vision (ECCV), October, 2022
● “NTT-PIM: Row-Centric Architecture and Mapping for Efficient Number-Theoretic Transform on PIM,” Jaewoo Park, Sugil Lee and Jongeun Lee, Proc. of the 60th Annual ACM/IEEE Design Automation Conference (DAC), pp. 1-6, July 11, 2023.
● “Squeezing Accumulators in Binary Neural Networks for Extremely Resource-Constrained Applications,” Azat Azamat, Jaewoo Park and Jongeun Lee, Proc. of International Conference on Computer-Aided Design (ICCAD), October 30-November 4, 2022.
● “Automated Log-Scale Quantization for Low-Cost Deep Neural Networks,” Sangyun Oh, Hyeonuk Sim, Sugil Lee and Jongeun Lee, Proc. of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 742-751, June 19-25, 2021.

[Awards/ Honors/ Memberships]

● Research Excellence Award, UNIST, 2020.
● Best Paper Award, Korean Conference on Semiconductors, 2014.
● Publication Chair, ACM/IEEE ASP-DAC, 2024.
● Technical Program Committee, ACM/IEEE ICCAD, 2023.
● IEEE Member (since 2001)
● ACM Member (since 2008)