A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks
arXiv:2604.24810v3 Announce Type: replace Abstract: Edge computing environments impose strict constraints on energy consumption and latency, making the deployment of deep neural networks a significant challenge. Therefore, smart and adaptive inference strategies that dynamically balance computational cost or latency with predictive accuracy are critical in edge computing scenarios. In this work, we build on Adaptive Deep Neural Networks (ADNNs) that employ the Multi-Armed Bandit (MAB) framework. Current literature leverages the first version of the Upper Confidence Bound (UCB1) strategy to dyn
The increasing prevalence of edge computing and the growing demand for efficient AI deployments in constrained environments are driving research into adaptive neural networks.
Optimizing AI performance on edge devices is crucial for widespread machine learning adoption in IoT, industrial applications, and autonomous systems, directly impacting scalability and accessibility.
This research contributes to making powerful AI models more viable for energy- and latency-sensitive applications, potentially lowering barriers to entry for advanced AI deployment.
- · Edge computing providers
- · IoT device manufacturers
- · AI developers
- · Industrial automation
- · Traditional heavy-compute cloud AI services
- · Inefficient deep learning models
Improved performance and efficiency of AI on edge devices.
Broader adoption of AI in resource-constrained environments, leading to new applications and markets.
Reduced dependency on centralized cloud infrastructure for certain AI tasks, decentralizing AI compute power.
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