Applications of Deep Neural Networks for Ultra Low Power IoT
| Autor: | Paul N. Whatmough, Patrick Hansen, David Brooks, Gu-Yeon Wei, Niamh Mulholland, Sreela Kodali |
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| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science Deep learning 020208 electrical & electronic engineering 02 engineering and technology Facial recognition system Activity recognition CMOS Computer architecture Keyword spotting 0202 electrical engineering electronic engineering information engineering Hardware acceleration 020201 artificial intelligence & image processing Artificial intelligence business Energy (signal processing) |
| Zdroj: | ICCD |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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