Gen-TAS：面向FPGA-GPP异构系统的生成式AI辅助软硬件任务分配框架
Gen-TAS: A Generative AI-Aided Hardware-Software Task Allocation Framework for FPGA-GPP Heterogeneous Systems
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中文总结 AI 辅助
Gen-TAS是一种知识增强LLM框架，结合任务图分析与RAG技术，为FPGA-GPP异构系统生成用户特定的可解释任务分配策略，在CNN、SDR工作负载实验中实现稳定的需求驱动分配，延迟导向场景下获显著加速。
中文摘要 AI 辅助
FPGA-GPP异构系统兼具软件的灵活性与可重配置硬件的性能及能效优势。然而，确定应用任务在通用处理器（GPP）还是现场可编程门阵列（FPGA）上运行，需要丰富的专业知识与大量设计空间探索，尤其当用户目标在延迟、通信、资源利用率及功耗等维度存在差异时。本文提出Gen-TAS，一种面向用户特定FPGA-GPP任务分配的知识增强大语言模型（LLM）框架。该框架结合任务图分析与检索增强生成（RAG）技术，将LLM的推理建立在历史实现知识的基础上，生成多种符合指定目标的可解释策略。通过人在回路选择机制与确定性后端，将LLM生成的决策与可复现的FPGA片上系统（SoC）实现相连接。针对卷积神经网络（CNN）与软件定义无线电（SDR）工作负载，在多种LLM上开展的实验表明，该框架可实现稳定的、符合需求驱动的任务分配。在以延迟为导向的目标下，遵循所选策略的实现相较于对应的全GPP基线分别实现了最高2.45倍与92.53倍的加速；而针对其他目标，框架会选择在加速性能与FPGA-GPP通信、资源利用率或FPGA功耗之间进行权衡的策略。
英文摘要
FPGA-GPP heterogeneous systems combine software flexibility with the performance and energy efficiency of reconfigurable hardware. However, determining which application tasks should execute on the GPP or FPGA requires extensive expertise and design-space exploration, particularly when user objectives vary across latency, communication, resource utilisation, and power. This paper proposes Gen-TAS, a knowledge-grounded LLM framework for user-specific FPGA-GPP task allocation. By combining task-graph analysis with RAG, Gen-TAS grounds LLM reasoning in historical implementation knowledge and generates multiple explainable strategies tailored to the specified objectives. Human-in-the-loop selection and a deterministic backend connect LLM-generated decisions to reproducible FPGA SoC implementations. Experiments on CNN and SDR workloads across multiple LLMs demonstrate stable, requirement-driven allocation. Under latency-oriented objectives, implementations following the selected strategies achieve speedups of up to 2.45$\times$ and 92.53$\times$, respectively, relative to the corresponding all-GPP baselines while other objectives select strategies that trade some acceleration performance for FPGA-GPP communication, resource utilisation, or FPGA power.
