Presentation
Invited: LLM4AIGChip: Harnessing Large Language Models Towards Automation of AI Accelerator Design
Description"In the rapidly evolving field of Artificial Intelligence (AI), the demand for efficient AI hardware accelerators is increasingly paramount. However, the complex and labor-intensive process of designing these accelerators presents significant challenges, hindering the pace of development in line with the evolving AI landscape. To address this, we propose the LLM4AIGChip initiative, which aims to leverage the extraordinary capabilities of Large Language Models (LLMs) to revolutionize AI accelerator design and enhance its accessibility. LLM4AIGChip consists of two key components: Data4AIGChip and GPT4AIGChip, each targeting fundamental bottlenecks in LLM-assisted AI accelerator design. Specifically, Data4AIGChip tackles the issues of dataset scarcity and quality in LLM-assisted hardware design by creating high-quality, specialized datasets, thereby augmenting the effectiveness of LLMs in AI accelerator design. In contrast, GPT4AIGChip focuses on employing LLMs to automate the design and verification processes of AI accelerators, leveraging the advanced capabilities of LLMs to streamline and simplify these tasks.
Collectively, these integrated frameworks mark a substantial advancement in AI accelerator design. They not only enhance the efficiency and accessibility of AI accelerator development but also serve as a bridge between AI algorithmic advancements and hardware innovation."
Collectively, these integrated frameworks mark a substantial advancement in AI accelerator design. They not only enhance the efficiency and accessibility of AI accelerator development but also serve as a bridge between AI algorithmic advancements and hardware innovation."
Event Type
Special Session (Research)
TimeWednesday, June 262:30pm - 3:00pm PDT
Location3006, 3rd Floor
AI