Hardware Generation with High Flexibility using Reinforcement Learning Enhanced LLMs

Yifang Zhao, Weimin Fu, Shijie Li, Yi-Xiang Hu, Xiaolong Guo, Yier Jin

Abstract: The increasing complexity of integrated circuit design requires customizing Power, Performance, and Area (PPA) metrics according to different application demands. However, most engineers cannot anticipate requirements early in the design process, often discovering mismatches only after synthesis, necessitating iterative optimization or redesign. Some works have shown the promising capabilities of large language models (LLMs) in hardware design generation tasks, but they fail to tackle the PPA trade-off problem. In this work, we propose an LLM-based reinforcement learning framework, PPA-RTL, aiming to introduce LLMs as a cutting-edge automation tool by directly incorporating post-synthesis metrics PPA into the hardware design generation phase. We design PPA metrics as reward feedback to guide the model in producing designs aligned with specific optimization objectives across various scenarios. The experimental results demonstrate that PPARTL models, optimized for Power, Performance, Area, or their various combinations, significantly improve in achieving the desired trade-offs, making PPA-RTL applicable to a variety of application scenarios and project constraints.

External IDs:dblp:conf/dac/ZhaoFLHGJ25