AgRefactor, a novel LLM-based multi-agent workflow, aims to improve High-Level Synthesis (HLS) compatibility and performance in software refactoring. Developed by Yang Zou and colleagues, this innovative system was introduced on June 29, 2026, addressing longstanding challenges in converting software into synthesizable HLS code.
Overview of AgRefactor's Capabilities
The transition from software to HLS-compatible programs has been complicated by strict language support and differences in programming practices. AgRefactor tackles these issues by incorporating a self-evolving memory system that enhances knowledge retrieval across tasks.
By leveraging automated refactoring tools, AgRefactor enables agents to optimize the balance between LLM-driven rewrites and efficient tool-based transformations, thus improving overall performance.
Performance Evaluation and Results
In rigorous tests involving 11 real-world benchmarks, AgRefactor demonstrated its capability by outperforming or matching the state-of-the-art automated refactoring tool and a strong LLM-based baseline. Notably, it achieved a 6.51x speedup over the leading pragma tuning tool and a 1.20x speedup over optimized open-source designs with minimal resource overhead.
- 9 out of 11 benchmarks tested
- 5-10x longer than previous complex cases
- Less than 20% extra resources used
The Future of Automated Refactoring
AgRefactor's fully-automated and open-source nature positions it as a promising solution for software developers seeking to streamline the HLS conversion process. The integration of self-evolving agents marks a significant advancement in automated refactoring technologies, paving the way for future innovations.
🤖 This article was rewritten by Feed and Figures' editorial AI from a report originally published by arXiv AI. Facts and quotes are preserved from the original; the rewrite focuses on clarity and structure. For the unedited original, see the source link below.
