LLM-Driven Design Space Exploration of FPGA-based Accelerators

• URL: http://arxiv.org/abs/2605.05920v1
• Date: Thu, 07 May 2026 09:29:59 GMT
• Title: LLM-Driven Design Space Exploration of FPGA-based Accelerators
• Abstract summary: SECDA-DSE is a framework that integrates Large Language Models into the SECDA ecosystem to automate the design space exploration of FPGA-based accelerators.<n>We demonstrate the feasibility of SECDA-DSE through an initial high-level synthesis based evaluation of a generated accelerator design.
• Score: 0.5133143243716792
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Designing field-programmable gate array (FPGA)-based accelerators for modern artificial intelligence workloads requires navigating a large and complex hardware design space encompassing architectural parameters, dataflow strategies, and memory hierarchies, making the process time-consuming and resource-intensive. While the SECDA methodology enables rapid hardware-software co-design of accelerators through SystemC simulation and FPGA execution, identifying optimal accelerator configurations still requires substantial manual effort and domain expertise. This work presents SECDA-DSE, a framework that integrates Large Language Models (LLMs) into the SECDA ecosystem, comprising tools built around SECDA to automate the design space exploration (DSE) of FPGA-based accelerators. SECDA-DSE combines a structured DSE Explorer for generating accelerator configurations with an LLM Stack that performs reasoning-guided exploration using retrieval-augmented generation and chain-of-thought prompting, alongside a feedback loop that enables reinforced fine-tuning for continuous improvement. We demonstrate the feasibility of SECDA-DSE through an initial high-level synthesis based evaluation of a generated accelerator design that meets synthesis timing and resource constraints on an Zynq-7000 FPGA.

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