arXiv AI
Exposing AI Inference as a Telco Network API over the 5G User Plane
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arXiv AI
Oct 27
Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networks
The paper proposes a Multi-Agentic AI framework to enable fairness‑aware and accelerated inference of large, heterogeneous multi‑modal language models on mobile edge networks. It introduces a Global Planning Agent, a Prompt Scheduling Agent, and multiple on‑node LM Deployment Agents that jointly optimize prompt routing and model deployment using natural language reasoning over telemetry and historical data. Experiments on a city‑wide testbed show a 42.9% reduction in end‑to‑end latency versus a state‑of‑the‑art baseline and an 80.6% reduction versus a full‑activation baseline, while preserving high fairness (Jain's index 0.925).
arXiv AI
Aug 19
Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum
The paper introduces a dynamic framework for partitioning neural network layers across a heterogeneous edge‑cloud continuum, adapting to runtime changes in network conditions and device capabilities. It profiles models at startup, measures link quality, and periodically re‑evaluates the partitioning to optimize performance. Experiments on a Raspberry Pi, laptop, and desktop using VGG16, AlexNet, and MobileNetV2 demonstrate energy savings of 27.09–35.82% and latency reductions of 6.34–22.92% over static partitioning.
arXiv Machine Learning
Sep 15
Partition-Aware Scheduling for Mobile Heterogeneous Inference Co-Execution
The paper introduces a partition-aware scheduling framework for mobile inference on heterogeneous platforms that combines mobile GPUs and multiple CPU core clusters. It jointly optimizes operator partitioning, device assignment, and execution order for static DAGs of operators, such as those in CNNs or vision transformers. An online iterative search approach decomposes large DAGs into stages, targets critical operators, and uses latency predictors to avoid exhaustive profiling, achieving near‑optimal latency with minimal scheduling overhead.
arXiv AI
Sep 15
MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks
MANE is a distributed inference framework that uses a multi‑path tail architecture to allow dynamic accuracy–throughput trade‑offs during edge onloading of deep neural networks. It introduces a novel multi‑path model, a three‑stage training scheme with Joint Head Network Distillation loss, and a hysteresis‑based scheduler with an equitable device‑fallback policy. The system achieves over 80% SLO satisfaction and 6pp higher accuracy than on‑device alternatives while supporting up to 40 concurrent devices.
arXiv Machine Learning
Jun 5
Advanced AI Service Provisioning in O-RAN through LLM Engine Integration
arXiv:2605. 23809v2 Announce Type: replace-cross Abstract: The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual.
arXiv AI
Oct 15
EV-JEPA: Elastic V-JEPA for Any Device and Cross-Device Inference
arXiv:2610.14324v1 Announce Type: cross Abstract: Foundation models like V-JEPA are increasingly deployed across a heterogeneous edge-cloud continuum spanning smart glasses, phones, robots, and serve...