  Pruning is a legitimate method for reducing the size of a neural network to
fit in low SWaP hardware, but the networks must be trained and pruned offline.
We propose an algorithm, Artificial Neurogenesis (ANG), that grows rather than
prunes the network and enables neural networks to be trained and executed in
low SWaP embedded hardware. ANG accomplishes this by using the training data to
determine critical connections between layers before the actual training takes
place. Our experiments use a modified LeNet-5 as a baseline neural network that
achieves a test accuracy of 98.74% using a total of 61,160 weights. An ANG
grown network achieves a test accuracy of 98.80% with only 21,211 weights.

Neural and Evolutionary Computing;Machine Learning