arXiv:2409.03777v3 Announce Type: replace-cross Abstract: Deep convolutional neural networks (CNNs) have achieved impressive performance in many computer vision tasks. However, their large model sizes require heavy computational resources, making pruning redundant filters from existing pre-trained CNNs an essential task in developing efficient models for resource-constrained devices. Whole-network filter pruning algorithms prune varying fractions of filters from each layer, hence providing greater flexibility. Current whole-network pruning methods are either computationally expensive due to th
The continuous growth in size and computational demands of advanced AI models necessitates more efficient methods for model deployment, particularly for resource-constrained environments.
This development allows for the deployment of powerful CNNs on a wider range of devices, lowering the barrier to entry for advanced AI applications and improving energy efficiency.
The ability to more efficiently prune convolutional neural networks opens up new possibilities for edge AI, embedded systems, and sustainable AI development.
- · Edge device manufacturers
- · AI developers targeting mobile/IoT
- · Energy-conscious data centers
- · Computer vision applications
- · Companies reliant solely on large, unoptimized models
- · Providers of high-cost, high-power compute solutions
More powerful AI models become accessible on resource-limited hardware.
The proliferation of efficient AI leads to new applications and increased automation in distributed systems.
Reduced compute requirements contribute to lower energy consumption for AI inference, impacting data center growth and energy grids.
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Read at arXiv cs.LG