A2H-MAS: An Algorithm-to-HLS Multi-Agent System for Automated and Reliable FPGA Implementation

Bridging the gap between high-level algorithm development and efficient FPGA implementation remains a fundamental challenge, particularly in latency- and resource-constrained domains such as wireless communications. In practice, many signal processing and communication algorithms are first developed and validated at the software level, where rapid iteration and numerical flexibility are critical, making MATLAB a de facto environment for algorithm prototyping and verification. To deploy these algorithms on FPGA platforms, High-Level Synthesis (HLS) is commonly adopted as an intermediate step that enables hardware generation from high-level descriptions. Although HLS significantly improves productivity compared with register-transfer level design, translating MATLAB models into high-quality HLS implementations still requires substantial manual effort and deep domain expertise. Designers must carefully restructure computation patterns, redesign dataflows, and tune synthesis parameters to meet strict performance and resource constraints. Recent advances in large language models (LLMs) suggest a promising direction for automating this translation process. However, current LLM-based approaches remain unreliable in complex hardware design workflows, as they suffer from hallucinations, forgetting, limited domain expertise, and often overlook key performance metrics such as latency, throughput, and resource utilization.