Discover how pruning can optimize deep learning systems while maintaining their performance and accuracy in AI tasks.
Deep learning and AI systems are steadily on the rise in terms of usage, thanks to their capability of automating complex computational tasks such as image recognition, computer vision, and natural language processing. However, these systems use billions of parameters and require a gigantic amount of memory usage, which further spikes computational costs.
”As AI becomes more prevalent in our lives, these results can help humans in optimized usage of AI systems – both from a memory and energy perspective.Some basics on pruningPruning neural networks involves systematically removing redundant parameters to reduce model size, memory usage, and computational costs. The process involves selecting any one out of the two types of pruning: structured pruning and unstructured pruning.
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