Heterogeneous embedded systems, with diverse computing elements and accelerators such as FPGAs, offer a promising platform for fast and flexible ML inference, which is crucial for services such as autonomous driving and augmented reality, where delays can be costly. However, efficiently allocating computational resources for deep learning applications in FPGA-based systems is a challenging task. A Deep Learning Processor Unit (DPU) is a parameterizable FPGA-based accelerator module optimized for ML inference. It supports a wide range of ML models and can be instantiated multiple times within a single FPGA to enable concurrent execution. This paper introduces DPUConfig, a novel runtime management framework, based on a custom Reinforcement Learning (RL) agent, that dynamically selects optimal DPU configurations by leveraging real-time telemetry data monitoring, system utilization, power consumption, and application performance to inform its configuration selection decisions. The experimental evaluation demonstrates that the RL agent achieves energy efficiency 95% (on average) of the optimal attainable energy efficiency for several CNN models on the Xilinx Zynq UltraScale+ MPSoC ZCU102.

翻译：异构嵌入式系统集成了多样化的计算单元与FPGA等加速器，为快速灵活的机器学习推理提供了理想平台，这对自动驾驶和增强现实等延迟敏感型服务至关重要。然而，在基于FPGA的系统中为深度学习应用高效分配计算资源仍具挑战性。深度学习处理器单元（DPU）是一种可参数化的基于FPGA的加速器模块，专为机器学习推理优化设计。它支持广泛的机器学习模型，并可在单个FPGA内多次实例化以实现并行执行。本文提出DPUConfig——一种基于定制强化学习（RL）智能体的新型运行时管理框架，该框架通过实时遥测数据监测、系统利用率、功耗和应用程序性能来动态选择最优DPU配置。实验评估表明，在Xilinx Zynq UltraScale+ MPSoC ZCU102平台上，针对多种CNN模型，该强化学习智能体实现的能效平均可达理论最优能效的95%。