Get Started
Home
Topics
Search
Library
Research questionHow can predictive ML inference share wireless access points without degrading packet services under load?Wireless access points share CPU and memory between prediction, packet processing, radio operations, and client management. Measurements on proxy hardware or in isolation may therefore miss inference delays, memory differences, and network-service degradation under concurrent load.
AI
Evaluation & Benchmarks
Inference Optimization
Machine Learning
Small / On-device Models
Technology
Latest papersRecent research connected to this question, newest first.Network-Aware Forecasting on Wireless Access PointsApplies to predictive ML inference running directly on enterprise wireless access points. The source examines qualification of models and execution paths on target APs, followed by validation under packet-service and forecasting workloads; its benchmarks compare APs with Raspberry Pi 5 hardware, multiple model implementations, two similarly sized forecasting foundation models, and parallel streams under network saturation.research paper · Sep 2, 2026
Related questions
How can diffusion language models support reliable mobile-edge agents under tight latency and resource constraints?How can on-device LLM inference overlap weight I/O and computation under tight DRAM without stale sparsity decisions?How should wireless foundation models be evaluated for transferable representations under task and distribution shifts?How can Wi-Fi-based human-activity recognition reduce training and inference memory without sacrificing accuracy?