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The Breakthrough Memory Solutions for Improved Performance on LLM Inference

Authors :
Kim, Byeongho
Cha, Sanghoon
Park, Sangsoo
Lee, Jieun
Lee, Sukhan
Kang, Shin-haeng
So, Jinin
Kim, Kyungsoo
Jung, Jin
Lee, Jong-Geon
Lee, Sunjung
Paik, Yoonah
Kim, Hyeonsu
Kim, Jin-Seong
Lee, Won-Jo
Ro, Yuhwan
Cho, YeonGon
Kim, Jin Hyun
Song, JoonHo
Yu, Jaehoon
Lee, Seungwon
Cho, Jeonghyeon
Sohn, Kyomin
Source :
IEEE Micro; 2024, Vol. 44 Issue: 3 p40-48, 9p
Publication Year :
2024

Abstract

Large language models (LLMs) have changed our lives, but they require unprecedented computing resources—especially large memory capacity and high bandwidth to process weights. However, while the logic process was developing, the speed of development of the memory process could not keep up, causing problems that resulted in the performance of LLMs being hindered by memory. Samsung has introduced breakthrough processing-in-memory/processing-near-memory (PIM/PNM) solutions that enhance the main memory bandwidth. With the high bandwidth memory PIM-based GPU-cluster system and LPDDR5-PIM-based system, the performance of transformer-based LLMs improved by up to 1.9$ \times $× and 2.7$ \times $×, respectively. The Compute eXpress Link (CXL)-based PNM solution serves memory-centric computing systems by implementing logic inside the CXL memory controller. This results in a performance gain of more than 4.4$ \times $× with an energy reduction of about 53% with PNM. Furthermore, we provide PIM/PNM software stacks, including an AI compiler targeting the acceleration of AI models.

Details

Language :
English
ISSN :
02721732
Volume :
44
Issue :
3
Database :
Supplemental Index
Journal :
IEEE Micro
Publication Type :
Periodical
Accession number :
ejs66751901
Full Text :
https://doi.org/10.1109/MM.2024.3375352