@conference {Kodali2017,
	title = {Applications of Deep Neural Networks for Ultra Low Power IoT},
	booktitle = {International Conference on Computer Design},
	year = {2017},
	abstract = {IoT devices are increasing in prevalence and popularity, becoming an indispensable part of daily life. Despite the stringent energy and computational constraints of IoT systems, specialized hardware can enable energy-efficient sensor-data classification in an increasingly diverse range of IoT applications. This paper demonstrates seven different IoT applications using a fully-connected deep neural network (FC-NN) accelerator on 28nm CMOS. The applications include audio keyword spotting, face recognition, and human activity recognition. For each application, a FC-NN model was trained from a preprocessed dataset and mapped to the accelerator. Experimental results indicate the models retained their state-of-the-art accuracy on the accelerator across a broad range of frequencies and voltages. Real-time energy results for the applications were found to be on the order of 100nJ per inference or lower.},
	keywords = {accelerators, dnn, machine learning},
	url = {https://doi.org/10.1109/ICCD.2017.102},
	author = {Sreela Kodali and Patrick Hansen and Niamh Mulholland and Paul Whatmough and David Brooks and Gu  Wei}
}
