 Researchers unveil a pruning algorithm to shrink deep learning models
This technique figures out how to find a subset of neural network edge weights that can replicate most of the full model's performance, and is indeed quite simple. The catch is that this sparse subset of NN edge weights has no structure, so it doesn't get efficient execution on a GPU. If you're on a CPU with no specialized matrix math, this does in fact cut down on execution costs, but if you have an embedded ML chip, this doesn't really help.<p>Distillation is another shrinkage technique that is also pretty foolproof, but allows you to pick an arbitrary architecture - this way, you can make full use of whatever hardware you're deploying too.