The paper investigates how enterprise wireless access points (APs) can run predictive machine learning models while sharing CPU and memory with essential networking tasks. It introduces the concept of network‑aware deployability, requiring models to qualify for the AP’s hardware and to be validated under real packet‑service constraints. Benchmarks reveal that models can run 6–19× slower and use up to 22% more memory on an AP compared to a Raspberry Pi 5, and that even similarly sized forecasting models can differ by 19× in latency, leading to significant increases in round‑trip time and throughput degradation under load.
By Niloo Bahadori, Swadhin Pradhan, Peiman Amini
Enterprise wireless access points (APs) are promising platforms for predictive machine learning (ML), but their primary responsibility remains providing wireless connectivity and network services. Pre...
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