LLMs automate FPGA accelerator design space exploration with SECDA-DSE framework

Researchers have developed SECDA-DSE, a new framework that leverages Large Language Models (LLMs) to automate the design space exploration of FPGA-based accelerators. This system integrates LLMs with existing SECDA tools to navigate the complex hardware design process, which typically requires significant manual effort and expertise. The framework uses retrieval-augmented generation and chain-of-thought prompting for reasoning-guided exploration, incorporating a feedback loop for continuous improvement. Initial evaluations on a Zynq-7000 FPGA demonstrated that the generated accelerator designs meet synthesis timing and resource constraints. AI

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IMPACT Automates complex hardware design for AI workloads, potentially accelerating the development of specialized AI accelerators.

RANK_REASON This is a research paper detailing a new framework for hardware design automation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• LLM
• FPGA
• SECDA
• SECDA-DSE
• Zynq-7000

• paper
• infra