Designing an ML-Friendly Wireless Physical Layer for Low-Power IoT

With the advent of low-power Internet of Things (IoT), there is an increase in interest for designing inference systems in the cloud that aggregate and perform machine learning tasks from the low-power sensor data. Yet, unlike traditional mobile devices, low-power clients are too battery constrained to transmit large amounts of data within short time spans, as needed for many complex inference models. In this paper, we present a vision for bridging the gap between the power-starved low-power clients and the data-starved inference engines in the cloud. We present a mechanism that takes into account the battery life of these clients and how it affects the traditional inference models.