Search Results for author: Yongkee Kwon

Found 3 papers, 1 papers with code

LoL-PIM: Long-Context LLM Decoding with Scalable DRAM-PIM System

no code implementations28 Dec 2024 Hyucksung Kwon, Kyungmo Koo, Janghyeon Kim, Woongkyu Lee, Minjae Lee, Hyungdeok Lee, Yousub Jung, JaeHan Park, Yosub Song, Byeongsu Yang, Haerang Choi, Guhyun Kim, Jongsoon Won, Woojae Shin, Changhyun Kim, Gyeongcheol Shin, Yongkee Kwon, Ilkon Kim, Euicheol Lim, John Kim, Jungwook Choi

Processing-in-Memory (PIM) maximizes memory bandwidth by moving compute to the data and can address the memory bandwidth challenges; however, PIM is not necessarily scalable to accelerate long-context LLM because of limited per-module memory capacity and the inflexibility of fixed-functional unit PIM architecture and static memory management.

Management

Darwin: A DRAM-based Multi-level Processing-in-Memory Architecture for Data Analytics

no code implementations23 May 2023 Donghyuk Kim, Jae-Young Kim, Wontak Han, Jongsoon Won, Haerang Choi, Yongkee Kwon, Joo-Young Kim

In this paper, we propose Darwin, a practical LRDIMM-based multi-level PIM architecture for data analytics, which fully exploits the internal bandwidth of DRAM using the bank-, bank group-, chip-, and rank-level parallelisms.

Mini-batch Serialization: CNN Training with Inter-layer Data Reuse

1 code implementation30 Sep 2018 Sangkug Lym, Armand Behroozi, Wei Wen, Ge Li, Yongkee Kwon, Mattan Erez

Training convolutional neural networks (CNNs) requires intense computations and high memory bandwidth.

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