SECDA-DSE integrates LLMs using retrieval-augmented generation and chain-of-thought prompting to automate design space exploration for FPGA-based AI accelerators, demonstrated feasible by synthesizing one valid design on a Zynq-7000 FPGA.
arXiv:2603.05904 [cs.AR]https://arxiv.org/abs/2603
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LLM-Driven Design Space Exploration of FPGA-based Accelerators
SECDA-DSE integrates LLMs using retrieval-augmented generation and chain-of-thought prompting to automate design space exploration for FPGA-based AI accelerators, demonstrated feasible by synthesizing one valid design on a Zynq-7000 FPGA.