arXiv:2606.07615v1 Announce Type: new Abstract: Deep neural networks often contain redundant hidden units. Removing individual weights can reduce parameter count, but unstructured sparsity is not always easy to exploit in standard dense implementations. This paper develops a structured pruning framework in which complete neurons are removed using multi-armed bandit (MAB) algorithms. Each candidate neuron is treated as an arm; pulling an arm temporarily masks that neuron, measures the change in loss on a sampled mini-batch, restores the neuron, and updates an estimate of its safe-removal reward
The continuous growth in deep neural network complexity necessitates more efficient architectural optimization techniques, making pruning increasingly relevant for practical application.
Improving the efficiency of deep neural networks through structured pruning can lead to significant reductions in computational cost and energy consumption, impacting the scalability and deployability of AI models.
This research introduces a novel framework for more effective and structured neuron pruning, potentially enabling the deployment of larger models on resource-constrained hardware or reducing the compute required for existing models.
- · AI developers
- · Cloud computing providers (through efficiency gains)
- · Edge AI hardware manufacturers
- · Developers reliant on unstructured pruning methods
- · Less energy-efficient AI training approaches
More efficient and compact deep neural networks will be developed and deployed.
Reduced computational demands could lower the barrier to entry for AI development and deployment, particularly in specialized domains.
Increased global access to advanced AI capabilities due to lower resource requirements could accelerate AI adoption across various industries and regions.
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Read at arXiv cs.LG