A2H-MAS: An Algorithm-to-HLS Multi-Agent System for Automated and Reliable FPGA Implementation
Executive Summary
The paper introduces A2H-MAS, a multi-agent system designed to automate and enhance the reliability of converting high-level algorithms, particularly MATLAB models, into efficient FPGA implementations. Addressing challenges like manual effort, deep domain expertise requirements, and LLM unreliability, A2H-MAS employs a modular, hierarchical approach. It decomposes the workflow into specialized agents, using execution-based validation at each stage. The system also adopts a dataflow-oriented decomposition for MATLAB programs, transforming memory-centric code into streaming, sample-based implementations suitable for FPGA. Evaluated on wireless communication algorithms (5G NR, WLAN synchronization), A2H-MAS demonstrates consistent generation of functionally correct, resource-efficient, and latency-optimized HLS designs, marking a practical and scalable solution for real-world FPGA design workflows.
Key Outcomes & Impact
Leveraging A2H-MAS drives significant improvements in FPGA implementation workflows.
0%
FPGA Efficiency Gain
0x
Development Time Reduction
0%
Reliability Score
Deep Analysis & Enterprise Applications
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Below are key insights from the paper, relevant to High-Level Synthesis, presented as dynamic modules.
98.5%
Reliability Score achieved through execution-based validation at every stage.
A2H-MAS Workflow Decomposition
Algorithm Level Decomposition
→
Structured Workflow Application
→
MATLAB-to-HLS Translation
→
Design Space Exploration
→
Final System Integration
| Feature | Traditional HLS | LLM-based | A2H-MAS |
|---|---|---|---|
| Productivity | Medium | High (unreliable) | High (reliable) |
| Reliability | High (manual) | Low (hallucinations) | High (automated) |
| Domain Expertise | High | Medium | Low (automated) |
| Scalability | Medium | Medium | High |
Application in Wireless Communication
A2H-MAS was successfully applied to complex wireless communication algorithms, including 5G NR synchronization signal block detection and WLAN synchronization. The system demonstrated consistent generation of functionally correct, resource-efficient, and latency-optimized HLS designs. This significantly reduces the typical months of manual effort required for such implementations, showing a clear pathway for accelerating development in critical domains.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A phased approach to integrate A2H-MAS into your existing design workflows for maximum impact.
Phase 1: Agent Definition & Interface Design
Establish specialized agents with clear responsibilities and standardized input/output interfaces. Develop initial validation protocols.
Phase 2: Dataflow-Oriented Decomposition
Implement the strategy to break down complex MATLAB programs into smaller, manageable computational modules suitable for streaming FPGA execution.
Phase 3: Automated HLS Translation & Optimization
Develop and refine the MATLAB-to-HLS translation agents, incorporating design space exploration and automated optimization techniques.
Phase 4: System Integration & Validation
Integrate all agents into a cohesive system, perform end-to-end testing, and ensure functional correctness and performance targets across target applications.
Phase 5: Performance Benchmarking & Refinement
Benchmark the system against traditional methods and fine-tune agents for optimal resource utilization, latency, and throughput.
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