I am a M.S. student in the department of Electrical Engineering at Korea University, advised by Prof. Yunho Oh.
My research interests lie in the areas of Computer Architecture and Systems, Memory Systems, and Artificial Intelligence (AI). Especially, my recent work has focused on bridging the gap between computer architecture and emerging AI algorithms via algorithm-hardware co-design.
Before I joined Korea University, I worked on developing memory-efficient continual learning framework with Dr. Suhyun Kim at Korea Institute of Science and Technology (KIST) in 2022. I received my B.S. with honors from Hanyang University in 2021.
Publications
ICCD
HammerFilter: Robust Protection and Low Hardware Overhead Method for RowHammer
Kwangrae Kim, Jeonghyun Woo, Junsu Kim, and Ki-Seok Chung
In 2021 IEEE 39th International Conference on Computer Design (ICCD) 2021
The continuous scaling-down of the dynamic random access memory (DRAM) manufacturing process has made it possible to improve DRAM density. However, it makes small DRAM cells susceptible to electromagnetic interference between nearby cells. Unless DRAM cells are adequately isolated from each other, the frequent switching access of some cells may lead to unintended bit flips in adjacent cells. This phenomenon is commonly referred to as RowHammer. It is often considered a security issue because unusually frequent accesses to a small set of rows generated by malicious attacks can cause bit flips. Such bit flips may also be caused by general applications. Although several solutions have been proposed, most approaches either incur excessive area overhead or exhibit limited prevention capabilities against maliciously crafted attack patterns. Therefore, the goals of this study are (1) to mitigate RowHammer, even when the number of aggressor rows increases and attack patterns become complicated, and (2) to implement the method with a low area overhead.We propose a robust hardware-based protection method for RowHammer attacks with a low hardware cost called HammerFilter, which employs a modified version of the counting bloom filter. It tracks all attacking rows efficiently by leveraging the fact that the counting bloom filter is a space-efficient data structure, and we add an operation, HALF-DELETE, to mitigate the energy overhead. According to our experimental results, the proposed method can completely prevent bit flips when facing artificially crafted attack patterns (five patterns in our experiments), whereas state-of-the-art probabilistic solutions can only mitigate less than 56% of bit flips on average. Furthermore, the proposed method has a much lower area cost compared to existing counter-based solutions (40.6× better than TWiCe and 2.3× better than Graphene).
@inproceedings{kim2021hammerfilter,title={HammerFilter: Robust Protection and Low Hardware Overhead Method for RowHammer},author={Kim, Kwangrae and Woo, Jeonghyun and Kim, Junsu and Chung, Ki-Seok},booktitle={2021 IEEE 39th International Conference on Computer Design (ICCD)},pages={212-219},doi={10.1109/ICCD53106.2021.00043},issn={2576-6996},kind={conference},year={2021},video={https://www.youtube.com/watch?v=X_O8YaOSKLE}}
