用大模型推理预测HLS设计的多项关键指标。
Agentic-HLS: An agentic reasoning based high-level synthesis system using large language models (AI for EDA workshop 2024)
- 基于思维链技术，让大模型分析HLS代码并预测性能。
- 模型越大，对资源利用率和运行时延的预测越准。
- 适合芯片设计自动化与大模型结合方向的研究者。
我们参加2024年机器学习芯片设计高阶综合（HLS）竞赛的目标是预测设计的有效性、运行周期数（延迟）、块存储器（util-BRAM）、查找表（uti-LUT）、触发器（util-FF）以及数字信号处理器（util-DSP）的利用率。采用大语言模型结合思维链（Chain-of-thought）技术完成分类与回归任务。实验表明，更大规模的模型在推理能力上显著提升。我们公开了提示词（prompts），并提出一个面向大语言模型的HLS基准评估任务。
原文摘要 · Abstract (English)
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.
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