CLIO: enabling automatic compilation of deep learning pipelines across IoT and cloud

Abstract

Recent years have seen dramatic advances in low-power neural accelerators that aim to bring deep learning analytics to IoT devices; simultaneously, there have been considerable advances in the design of low-power radios to enable efficient compute offload from IoT devices to the cloud. Neither is a panacea --- deep learning models are often too large for low-power accelerators and bandwidth needs are often too high for low-power radios. While there has been considerable work on deep learning for smartphone-class devices, these methods do not work well for small battery-powered IoT devices that are considerably more resource-constrained.