We show that structured pruning enables better end-to-end compression de- spite lower pruning ratio because it boosts the efficiency of the arithmetic coder. As a bonus, once decompressed, the network memory footprint is lower as well as its inference time.
We show that structured pruning enables better end-to-end compression despite lower pruning ratio because it boosts the efficiency of the arithmetic coder. As ...
We show that structured pruning enables better end-to-end compression de- spite lower pruning ratio because it boosts the efficiency of the arithmetic coder. As ...
Oct 10, 2018 · Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and ...
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What is structural pruning in neural networks?
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Pruning, in particular, has emerged as a popular and effective method for reducing the number of parameters in a neural network [3, 15,20].
Jun 4, 2024 · Structured pruning improves neural network efficiency by removing entire network structures (e.g. neurons or convolutional filters) which have ...
Pruning methods have shown to be effective at reduc- ing the size of deep neural networks while keeping accu- racy almost intact. Among the most effective ...
Oct 21, 2018 · We have further shown that the sparsity patterns in the pruned architecture can help us design more efficient architecture from scratch, and can ...
In this paper, we propose a structured pruning method by deriving pruning rate for each layer adaptively based on gradient and loss function. The proposed ...
Aug 18, 2023 · Pruning is a widely used technique for reducing the size of deep neural networks while maintaining their perfor- mance. However, such a ...