做FPGA加速器设计的团队终于有了自动化工具——SECDA-DSE用LLM替代人工试错，直接生成可执行的加速器配置。搞硬件-软件协同设计的人可以试试，能省下大量调参时间。
SECDA-DSE是一个将大语言模型（LLM）集成到SECDA生态中的框架，用于自动探索FPGA加速器的设计空间。它结合了结构化探索器（生成候选架构）和LLM堆栈（通过检索增强生成和思维链提示进行推理引导），并通过反馈循环实现迭代优化。论文在FPGA硬件上生成了三种加速器设计（逐元素向量乘法、2D卷积和矩阵转置），并成功执行。结果表明，SECDA-DSE能生成符合SECDA标准的加速器设计，并捕捉不同内核在计算并行性和数据移动之间的权衡。该框架显著减少了探索时间和人工专业知识需求，展示了LLM引导设计空间探索的潜力。
Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA
Designing FPGA-based accelerators for modern artificial intelligence workloads requires exploring a large and complex hardware design space that involves architectural parameters, data flow strategies, and memory hierarchies, making the process very time consuming. While existing methodologies such as SECDA enable rapid hardware-software co-design through SystemC simulation and FPGA execution, identifying efficient accelerator configurations remains a largely manual process requiring extensive domain knowledge. SECDA-DSE is a framework that integrates Large Language Models (LLMs) into the SECDA ecosystem to guide design space exploration (DSE) of FPGA-based accelerators. It combines a structured DSE Explorer for generating candidate architectures with an LLM Stack that performs reasoning-guided exploration using retrieval-augmented generation and chain-of-thought prompting, coupled with a feedback loop for iterative and reinforced refinement. Building on our previous work introducing SECDA-DSE, this paper extends its evaluation by generating three accelerator designs, including element-wise vector multiplication, 2D convolution, and matrix transpose, and performing end-to-end execution on FPGA hardware. The results show that SECDA-DSE can generate SECDA-compliant accelerator designs that are successfully synthesized and executed on FPGA hardware. Furthermore, the generated designs capture kernel-specific trade-offs between compute parallelism and data movement, highlighting the potential of LLM-guided exploration to adapt architectural configurations across diverse workloads while reducing exploration time and the need for extensive human expertise.