Leesup Kim

Title: The Innovative Contributions of Leesup Kim

Introduction

Leesup Kim is a prominent inventor based in Daejeon, South Korea. He has made significant contributions to the field of technology, particularly in the areas of incremental learning systems and memory devices. With a total of 6 patents to his name, Kim's work reflects a deep understanding of advanced computational methods.

Latest Patents

One of his latest patents is titled "Incremental learning system with selective weight updates." This innovative learning method involves setting a searching range of mask weights based on the distribution of mask weights of a binary mask corresponding to a filter of a pretrained model. It identifies a targeted mask weight within this range and updates it based on the binary mask and the pretrained model. Additionally, it updates a portion of the binary mask based on the newly updated targeted mask weight and a preset reference value.

Another notable patent is "Memory device supporting rank-level parallelism and memory system including the same." This memory system comprises a memory controller, multiple ranks, and a rank shared bus that connects the memory controller to the ranks. Each rank includes several banks and a rank bus that selectively transmits and receives data, enhancing the efficiency of memory operations.

Career Highlights

Throughout his career, Leesup Kim has worked with esteemed organizations such as the Korea Advanced Institute of Science and Technology and SK Hynix Inc. His experience in these institutions has allowed him to develop and refine his innovative ideas, contributing to advancements in technology.

Collaborations

Kim has collaborated with notable colleagues, including Wongyu Shin and Youngsuk Moon. These partnerships have fostered a creative environment that has led to the development of groundbreaking technologies.

Conclusion

Leesup Kim's contributions to the field of technology through his patents and collaborations highlight his role as an influential inventor. His work continues to inspire advancements in incremental learning and memory systems.