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

• URL: http://arxiv.org/abs/2601.08166v1
• Date: Tue, 13 Jan 2026 02:56:06 GMT
• Title: ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms
• Authors: Mohammad Pivezhandi, Mahdi Banisharif, Abusayeed Saifullah, Ali Jannesari,
• Abstract summary: We propose a model-based hierarchical multi-agent reinforcement learning (MARL) framework for thermal- and energy-aware scheduling on multi-core platforms.<n>First-decision latency is 8,300x faster than table-based profiling, enabling practical deployment in dynamic embedded systems.
• Score: 7.633618497843279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. We propose a model-based hierarchical multi-agent reinforcement learning (MARL) framework for thermal- and energy-aware scheduling on multi-core platforms. Two collaborative agents decompose the exponential action space, achieving 358ms latency for subsequent decisions. First decisions require 3.5 to 8.0s including one-time LLM feature extraction. An accurate environment model leverages regression techniques to predict thermal dynamics and performance states. When combined with LLM-extracted semantic features, the environment model enables zero-shot deployment for new workloads on trained platforms by generating synthetic training data without requiring workload-specific profiling samples. We introduce LLM-based semantic feature extraction that characterizes OpenMP programs through 13 code-level features without execution. The Dyna-Q-inspired framework integrates direct reinforcement learning with model-based planning, achieving 20x faster convergence than model-free methods. Experiments on BOTS and PolybenchC benchmarks across NVIDIA Jetson TX2, Jetson Orin NX, RubikPi, and Intel Core i7 demonstrate 7.09x better energy efficiency and 4.0x better makespan than Linux ondemand governor. First-decision latency is 8,300x faster than table-based profiling, enabling practical deployment in dynamic embedded systems.

Related papers

• Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference [2.649774320778185]
  We present DynaExq, a runtime system that treats expert precision as a first-class, dynamically managed resource.<n>We show that DynaExq deploys large LLMs on single 5090 and A6000 GPUs and improves accuracy by up to 4.03 points over static low-precision baselines.
  arXiv  Detail & Related papers  (2025-11-19T01:27:54Z)
• Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models [50.260693393896716]
  Diffusion models (DMs) are powerful generative models capable of producing high-fidelity images but constrained by high computational costs.<n>We propose Flexiffusion, a training-free NAS framework that jointly optimize generation schedules and model architectures without modifying pre-trained parameters.<n>Our work pioneers a resource-efficient paradigm for searching high-speed DMs without sacrificing quality.
  arXiv  Detail & Related papers  (2025-06-03T06:02:50Z)
• Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition [95.54406667705999]
  Pangu Embedded is an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs)<n>It addresses the significant computational costs and inference latency challenges prevalent in existing reasoning-optimized LLMs.<n>It delivers rapid responses and state-of-the-art reasoning quality within a single, unified model architecture.
  arXiv  Detail & Related papers  (2025-05-28T14:03:02Z)
• PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing [48.30406812516552]
  We introduce the PLM, a Peripheral Language Model, developed through a co-design process that jointly optimize model architecture and edge system constraints.<n>PLM employs a Multi-head Latent Attention mechanism and employs the squared ReLU activation function to encourage sparsity, thereby reducing peak memory footprint.<n> evaluation results demonstrate that PLM outperforms existing small language models trained on publicly available data.
  arXiv  Detail & Related papers  (2025-03-15T15:11:17Z)
• Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models [31.103832542711864]
  Balcony is a framework for depth-based dynamic inference.<n>It maintains the full model's performance while enabling real-time adaptation to different computational budgets.<n>Remarkably, we show that Balcony outperforms state-of-the-art methods such as Flextron and Layerskip.
  arXiv  Detail & Related papers  (2025-03-06T22:09:55Z)
• AutoHete: An Automatic and Efficient Heterogeneous Training System for LLMs [68.99086112477565]
  Transformer-based large language models (LLMs) have demonstrated exceptional capabilities in sequence modeling and text generation.<n>Existing heterogeneous training methods significantly expand the scale of trainable models but introduce substantial communication overheads and CPU workloads.<n>We propose AutoHete, an automatic and efficient heterogeneous training system compatible with both single- GPU and multi- GPU environments.
  arXiv  Detail & Related papers  (2025-02-27T14:46:22Z)
• Tackling the Dynamicity in a Production LLM Serving System with SOTA Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient Meta-kernels [12.77187564450236]
  We introduce XY-Serve, a versatile, Ascend native, end-to-end production large language model (LLM) serving system.<n>The core idea is an abstraction mechanism that smooths out the workload variability by decomposing computations into fine-grained meta primitives.<n>For GEMM, we introduce a virtual padding scheme that adapts to dynamic shape changes while using highly efficient GEMM primitives with assorted fixed tile sizes.
  arXiv  Detail & Related papers  (2024-12-24T02:27:44Z)
• Flextron: Many-in-One Flexible Large Language Model [85.93260172698398]
  We introduce Flextron, a network architecture and post-training model optimization framework supporting flexible model deployment. We present a sample-efficient training method and associated routing algorithms for transforming an existing trained LLM into a Flextron model. We demonstrate superior performance over multiple end-to-end trained variants and other state-of-the-art elastic networks, all with a single pretraining run that consumes a mere 7.63% tokens compared to original pretraining.
  arXiv  Detail & Related papers  (2024-06-11T01:16:10Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.