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4. The Breakthrough Memory Solutions for Improved Performance on LLM Inference

Author

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, and Sohn, Kyomin

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.

Published
2024
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