Pruning Convolutional Neural Networks for Resource Efficient Inference

Pruning Neural Networks: Make Big AI Models Faster and Smaller

Big AI models can be trimmed down so they run faster on phones and laptops, without losing much of what they know.
By quietly cutting away unneeded parts — called pruning — and then giving the model a little practice, the same brain works almost as well but uses far less power.
This method was tested when moving big models to new tasks, a process known as transfer learning, and it kept the results strong.
Sometimes you can get more than a 10x shrink in size in theory, and about five times smaller in real life, while keeping close to the original accuracy.
The neat trick uses simple clues from the model to decide what to cut, so it’s fast and easy to do.
That means apps that recognize gestures or flowers, or even big image systems, can become lighter and run on small devices.
Imagine your phone doing smart tasks quicker and using less battery — that is the promise of smarter speed and smaller models.
Try to picture big brains, but tidy and efficient now.

Read article comprehensive review in Paperium.net:
Pruning Convolutional Neural Networks for Resource Efficient Inference

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