LLM-Driven Design Space Exploration of FPGA-based Accelerators

Researchers present SECDA-DSE, an AI-driven framework that integrates Large Language Models into FPGA accelerator design to automate the complex process of hardware configuration optimization. The system combines structured design space exploration with LLM-powered reasoning and feedback loops, demonstrating practical feasibility through successful synthesis on a Zynq-7000 FPGA.

SECDA-DSE addresses a critical bottleneck in specialized hardware development: the manual, expertise-intensive process of designing FPGA accelerators for AI workloads. Modern machine learning inference requires custom hardware optimization across numerous architectural parameters, dataflow strategies, and memory configurations. The framework's integration of LLMs represents a significant methodological shift, automating what traditionally demanded experienced hardware engineers spending weeks or months navigating interconnected design tradeoffs.

The approach builds on existing SECDA methodology by adding intelligent automation layers. Retrieval-augmented generation helps the LLM access relevant design knowledge, while chain-of-thought prompting enables structured reasoning about configuration choices. The reinforced fine-tuning feedback loop allows the system to improve iteratively based on synthesis outcomes, creating a self-improving design assistant.

This development carries implications for accelerator democratization. Currently, FPGA acceleration remains accessible primarily to well-resourced organizations with hardware expertise. By reducing manual effort and domain knowledge requirements, SECDA-DSE could lower barriers to entry for smaller teams and startups developing specialized AI inference hardware. The successful synthesis results on consumer-grade Zynq devices suggest practical applicability beyond research environments.

The framework's effectiveness depends on LLM training quality and the comprehensiveness of its design knowledge base. Future iterations may extend to larger, more complex FPGA platforms and newer AI architectures. Integration with emerging chiplet-based and heterogeneous computing approaches would determine long-term industry relevance.

• →LLM-driven automation reduces manual effort in FPGA accelerator design through retrieval-augmented generation and chain-of-thought reasoning
• →The SECDA-DSE framework combines structured exploration with LLM intelligence and feedback loops for continuous design improvement
• →Successful high-level synthesis results demonstrate practical feasibility on real hardware platforms like Zynq-7000 FPGAs
• →The approach could democratize FPGA acceleration by reducing expertise barriers for smaller organizations
• →Reinforced fine-tuning enables self-improvement, potentially increasing design quality and efficiency over time