Agentic-HLS: An agentic reasoning based high-level synthesis system using large language models (AI for EDA workshop 2024)

Autor:	Oztas, Ali Emre, Jelodari, Mahdi
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Hardware Architecture
Druh dokumentu:	Working Paper
Popis:	Our aim for the ML Contest for Chip Design with HLS 2024 was to predict the validity, running latency in the form of cycle counts, utilization rate of BRAM (util-BRAM), utilization rate of lookup tables (uti-LUT), utilization rate of flip flops (util-FF), and the utilization rate of digital signal processors (util-DSP). We used Chain-of-thought techniques with large language models to perform classification and regression tasks. Our prediction is that with larger models reasoning was much improved. We release our prompts and propose a HLS benchmarking task for LLMs. Comment: AI4EDA co-located with 38th Conference on Neural Information Processing Systems (NeurIPS 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.01604 Zobrazit plný text záznamu View this record from Arxiv