动态电压和频率调节（DVFS）以及任务到核心的分配对于嵌入式系统中的热管理和平衡能耗与性能至关重要。现有方法要么依赖于基于利用率的启发式方法，忽略了停滞时间，要么需要大量的离线配置以生成表格，从而限制了运行时的适应性。基于分层多代理调度，我们贡献了基于模型的强化学习方法，该方法利用准确的环境模型来预测热动态和性能状态，从而生成合成训练数据，并比无模型方法收敛快20倍。我们引入了基于大语言模型（LLM）的语义特征提取，通过代码层面的特征来表征OpenMP程序，而无需执行，从而在不到5秒内实现对新工作负载的零样本部署。两个协作代理分解了指数级的动作空间，后续决策的延迟达到358毫秒。在Barcelona OpenMP任务集（BOTS）和PolybenchC基准测试中，跨NVIDIA Jetson TX2、Jetson Orin NX、RubikPi和Intel Core i7等平台的实验表明，与现有电源管理技术相比，能耗效率提高了7.09倍，任务完成时间缩短了4.0倍，决策延迟为358毫秒。
英文摘要：
Dynamic voltage and frequency scaling (DVFS) and task-to-core allocation are critical for thermal management and balancing energy and performance in embedded systems. Existing approaches either rely on utilization-based heuristics that overlook stall times, or require extensive offline profiling for table generation, preventing runtime adaptation. Building upon hierarchical multi-agent scheduling, we contribute model-based reinforcement learning with accurate environment models that predict thermal dynamics and performance states, enabling synthetic training data generation and converging 20 times faster than model-free methods. We introduce Large Language Model (LLM)-based semantic feature extraction that characterizes OpenMP programs through code-level features without execution, enabling zero-shot deployment for new workloads in under 5 seconds without workload-specific profiling. Two collaborative agents decompose the exponential action space, achieving 358ms latency for subsequent decisions. Experiments on Barcelona OpenMP Tasks Suite (BOTS) and PolybenchC benchmarks across NVIDIA Jetson TX2, Jetson Orin NX, RubikPi, and Intel Core i7 demonstrate 7.09 times better energy efficiency, 4.0 times better makespan, and 358ms decision latency compared to existing power management techniques.
ActKV: Efficient LLM Agents through Action-Guided KV Cache Management
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；BandAI, Bytedance（字节跳动BandAI）
