A Deep Learning Approach for Mobile Applications

Advances in Deep Learning have enabled computers to comprehend data in a variety of complex tasks. Yet, despite these advances, Deep Learning methods are often computationally intensive making them unsuitable for applications in portable or embedded Internet of Things devices. One example of this would be inferring behavior based on sensor data collected by a portable device. These types of scenarios often place challenging constraints on available storage and computational resources. A new approach is needed to allow for a more flexible application of Deep Learning methods that is compliant with reduced resource utilization based on the characteristics of the underlying platform.   A new framework developed at Rice University allows a performance-efficient realization of Deep Learning on resource-constrained devices. Resource utilization can be greatly influenced by the dimensionality of…

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