[2601.08166] ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms

Computer Science > Artificial Intelligence arXiv:2601.08166 (cs) [Submitted on 13 Jan 2026 (v1), last revised 28 Feb 2026 (this version, v2)] Title:ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms Authors:Mohammad Pivezhandi, Mahdi Banisharif, Abusayeed Saifullah, Ali Jannesari View a PDF of the paper titled ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms, by Mohammad Pivezhandi and 3 other authors View PDF HTML (experimental) Abstract: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 ...