Energy harvesting technology and ultra-low-power microcontrollers has led to the advent of tiny, battery-less devices, capable of sustainable, maintenance free operation. These devices can now run deep neural networks (DNN) locally, shifting intelligent decision making from the cloud to beyond the edge. However, as harvestable ambient energy is weak and unstable, battery-less devices operate intermittently, experiencing frequent power failures. This talk will first introduce the fundamental challenges with enabling deep learning on battery-less devices. Next, some of the work carried out in CITI's EMCLab to address these challenges will be highlighted, specifically introducing runtime software concepts for efficient intermittent DNN inference, and novel design automation frameworks that are able to design and optimize DNNs for intermittent systems.