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Many Internet-of-Things (IoT) applications demand fast and accurateunderstanding of a few key events in their surrounding environment. DeepConvolutional Neural Networks (CNNs) have emerged as an effective approach tounderstand speech, images, and similar high dimensional data types. Algorithmicperformance of modern CNNs, however, fundamentally relies on learningclass-agnostic hierarchical features that only exist in comprehensive trainingdatasets with many classes. As a result, fast inference using CNNs trained onsuch datasets is prohibitive for most resource-constrained IoT platforms. Tobridge this gap, we present a principled and practical methodology fordistilling a complex modern CNN that is trained to effectively recognize manydifferent classes of input data into an application-dependent essential corethat not only recognizes the few classes of interest to the applicationaccurately, but also runs efficiently on platforms with limited resources.Experimental results confirm that our approach strikes a favorable balancebetween classification accuracy (application constraint), inference efficiency(platform constraint), and productive development of new applications (businessconstraint).

Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms