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arXiv · 2505.12188

LLM-DSE: Searching Accelerator Parameters with LLM Agents

Hanyu Wang·Xinrui Wu·Zijian Ding·Su Zheng·Chengyue Wang·Neha Prakriya·Tony Nowatzki·Yizhou Sun·Jason Cong
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Abstract

Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware directive parameters remains a significant hurdle. Existing heuristic and learning-based methods struggle with adaptability and sample efficiency. We present LLM-DSE, a multi-agent framework designed specifically for optimizing HLS directives. Combining LLM with design space exploration (DSE), our explorer coordinates four agents: Router, Specialists, Arbitrator, and Critic. These multi-agent components interact with various tools to accelerate the optimization process. LLM-DSE leverages essential domain knowledge to identify efficient parameter combinations while maintaining adaptability through verbal learning from online interactions. Evaluations on the HLSyn dataset demonstrate that LLM-DSE achieves substantial $2.55\times$ performance gains over state-of-the-art methods, uncovering novel designs while reducing runtime. Ablation studies validate the effectiveness and necessity of the proposed agent interactions. Our code is open-sourced here: https://github.com/Nozidoali/LLM-DSE.

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Hanyu Wang, Xinrui Wu, Zijian Ding, Su Zheng, Chengyue Wang, Neha Prakriya, Tony Nowatzki, Yizhou Sun, Jason Cong. 2025-05-18. LLM-DSE: Searching Accelerator Parameters with LLM Agents. https://arxiv.org/abs/2505.12188

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