Applications of Deep Neural Networks for Ultra Low Power IoT

Citation:

Sreela Kodali, Patrick Hansen, Niamh Mulholland, Paul Whatmough, David Brooks, and Gu Wei. 11/5/2017. “Applications of Deep Neural Networks for Ultra Low Power IoT.” In International Conference on Computer Design. Publisher's Version

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.
Last updated on 04/29/2022