@inproceedings{d0193f8e31cd407d9221e45437545d20,
title = "Invited: New Solutions on LLM Acceleration, Optimization, and Application",
abstract = "Large Language Models (LLMs) have revolutionized a wide range of applications with their strong human-like understanding and creativity. Due to the continuously growing model size and complexity, LLM training and deployment have shown significant challenges, which often results in extremely high computational and storage costs and energy consumption. In this paper, we discuss the recent advancements and research directions on (1) LLM algorithm-level acceleration, (2) LLM-hardware co-design for improved system efficiency, (3) LLM-to-accelerator compilation for customized LLM accelerators, and (4) LLM-aided design for HLS (High-Level Synthesis) functional verification. For each aspect, we present the background study, our proposed solutions, and future directions. An extended version of this work can be found at: https://arxiv.org/abs/2406.10903.",
keywords = "Acceleration, Functional Verification, Hardware Design, High-Level Synthesis (HLS), Large Language Models (LLMs)",
author = "Yingbing Huang and Wan, \{Lily Jiaxin\} and Hanchen Ye and Manvi Jha and Jinghua Wang and Yuhong Li and Xiaofan Zhang and Deming Chen",
note = "This work is supported in part by the IBM-Illinois Discovery Accelerator Institute, AMD Center of Excellence at UIUC, AMD Heterogeneous Adaptive Compute Cluster (HACC) initiative, NSF 2117997 grant through the A3D3 institute, and Semiconductor Research Corporation (SRC) 2023-CT-3175 grant.; 61st ACM/IEEE Design Automation Conference, DAC 2024 ; Conference date: 23-06-2024 Through 27-06-2024",
year = "2024",
month = nov,
day = "7",
doi = "10.1145/3649329.3663517",
language = "English (US)",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024",
address = "United States",
}