  Wireless traffic attributable to machine learning (ML) inference workloads is
increasing with the proliferation of applications and smart wireless devices
leveraging ML inference. Owing to limited compute capabilities at these "edge"
devices, achieving high inference accuracy often requires coordination with a
remote compute node or "cloud" over the wireless cellular network. The accuracy
of this distributed inference is, thus, impacted by the communication rate and
reliability offered by the cellular network. In this paper, an analytical
framework is proposed to characterize inference accuracy as a function of
cellular network design. Using the developed framework, it is shown that
cellular network should be provisioned with a minimum density of access points
(APs) to guarantee a target inference accuracy, and the inference accuracy
achievable at asymptotically high AP density is limited by the air-interface
bandwidth. Furthermore, the minimum accuracy required of edge inference to
deliver a target inference accuracy is shown to be inversely proportional to
the density of APs and the bandwidth.
