arXiv:2606.30919v1 Announce Type: cross Abstract: Edge-cloud inference collaborations are often designed with a routing estimator that decides whether to offload each frame from weak models at the edge to stronger models in the cloud. Existing systems place the routing estimator after the weak detector, so the weak forward pass still runs even on frames that are later offloaded. In this paper, we argue that this weak-conditioned design can be suboptimal when the offload budget varies. First, we present a competitive weak-skipping estimator (0.153 GFLOPs, about 29x lighter than the weak detecto
The increasing prevalence of edge-cloud AI deployments and the need for more efficient resource utilization are driving innovation in routing algorithms.
This research directly addresses the efficiency and cost-effectiveness of AI inference, crucial for scaling complex models in real-world applications.
The proposed 'weak-skipping' approach optimizes AI inference by intelligently bypassing unnecessary weak model computations, leading to significant efficiency gains.
- · Edge AI providers
- · Cloud infrastructure providers
- · Companies deploying AI at scale (e.g., autonomous vehicles, IoT)
- · AI developers focused on efficiency
- · Companies with inefficient edge-cloud AI architectures
- · Providers of compute-heavy weak detection models
Reduced operational costs and latency for edge-cloud AI systems.
Accelerated adoption of more complex and higher-fidelity AI models in resource-constrained edge environments.
Enhanced overall energy efficiency of distributed AI networks, impacting sustainability metrics.
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Read at arXiv cs.AI