Adaptive Convolutional Neural Networks

What is it about?

Convolutional Neural Networks (CNNs) achieved state-of-the-art accuracy in many applications. Unfortunately their complexity prevent an effective deployment in portable devices. This work introduces a practical solution to design CNNs able to adapt to the context and scale their computational effort depending on the resources available. The proposed solution exploits the hierarchical organization of concepts that we, as humans, use during our everyday activities.

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Why is it important?

The proposed strategy is well suited for embedded AI technologies. It enables the portability of Convolutional Neural Networks on resource constrained IoT end-nodes. This is achieved by transforming static CNNs into adaptive, hence flexible, models that adapt their energy consumption. The results demonstrate remarkable improvements.

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