arXiv:2605.24025v1 Announce Type: cross Abstract: Large models have delivered remarkable performance across a wide range of perception and generation tasks, yet practical deployment is increasingly constrained by computational and memory budgets, as well as privacy requirements. Split execution alleviates these constraints by partitioning computation across devices, but it inevitably introduces intensive transmission and storage of intermediate features. Unlike conventional feature coding for CNNs that typically targets homogeneous spatial activation maps, modern large models generate heteroge
The proliferation of increasingly larger and more complex AI models is creating significant bottlenecks related to computational resources, memory, and data privacy, driving the need for distributed and efficient processing solutions.
Efficient feature coding for large models is critical for enabling their deployment in resource-constrained environments and for addressing growing privacy concerns, impacting the scalability and accessibility of advanced AI.
Approaches to feature coding are shifting from homogeneous spatial activation maps to accommodating the heterogeneous structures generated by modern large models, allowing for more practical and distributed AI execution.
- · Edge AI device manufacturers
- · Cloud providers offering distributed AI services
- · AI developers prioritizing efficiency and privacy
- · Companies reliant solely on monolithic model deployment
- · Legacy feature coding specialists
Improved efficiency and reduced memory footprint for large AI models.
Increased accessibility and wider deployment of advanced AI applications on diverse hardware, including edge devices.
Accelerated development of domain-specific, resource-optimized AI, potentially decentralizing AI power.
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Read at arXiv cs.LG