# ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms

arXiv: https://arxiv.org/abs/2601.08166 · HTML: https://mlsanity.com/paper/2601.08166/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 77 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Stochastic Neural Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1704.03012/) (2017)
- [A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends](https://mlsanity.com/paper/2311.10372/) (2023)
- [Online Normalization for Training Neural Networks](https://mlsanity.com/paper/1905.05894/) (2019)
- [PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals](https://mlsanity.com/paper/2006.00900/) (2020)
- [Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents](https://mlsanity.com/paper/1912.00498/) (2019)
- [DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference](https://mlsanity.com/paper/2306.01811/) (2023)
- [Multi-focus Attention Network for Efficient Deep Reinforcement Learning](https://mlsanity.com/paper/1712.04603/) (2017)
- [Predicting Code Coverage without Execution](https://mlsanity.com/paper/2307.13383/) (2023)
- [Can Large Language Models Predict Parallel Code Performance?](https://mlsanity.com/paper/2505.03988/) (2025)

## Abstract (arXiv)

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.