Maching learning

Learning sparse neural topologies for embedded avionic applications

Convolutional Neural Networks (CNNs) can be trained to achieve state-of-the-art performance in several computer vision tasks thanks to complex topologies with millions of learnable parameters. Such complex topologies limit however the chances to deploy such architectures on embedded devices, where installed memory and computational resources are limited. Nevertheless, once a network has been trained at a task, part of the connections between neurons can be dropped, yielding a sparse network topology with reduced memory foot print.

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