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arXiv CS.AI·May 25·5 min read
A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks
TL;DR
Edge computing presents challenges in deploying deep neural networks due to energy and latency constraints. This study introduces four Upper Confidence Bound (UCB) strategies for Adaptive Deep Neural Networks (ADNNs) to optimize accuracy and resource usage.
✦ Why It Matters
Engineers can leverage these UCB strategies to enhance neural network efficiency in edge computing applications.
Key Takeaways
- Implement UCB strategies to optimize neural network performance.
- Use UCB-Bayes for faster convergence in adaptive models.
- Balance accuracy with energy consumption using UCB-V and UCB-Tuned.
- Evaluate performance on benchmark datasets like CIFAR-10.
Full Summary
Edge computing environments require efficient deep neural network deployment due to limitations in energy and latency. To address this, Adaptive Deep Neural Networks (ADNNs) utilizing the Multi-Armed Bandit (MAB) framework were enhanced with four new Upper Confidence Bound (UCB) strategies: UCB-V, UCB-Tuned, UCB-Bayes, and UCB-BwK.
These strategies were tested on ResNet and MobileViT architectures using benchmark datasets like CIFAR-10 and CIFAR-100. Experimental results indicated that all strategies achieved sub-linear cumulative regret, with UCB-Bayes showing the fastest convergence.
UCB-V and UCB-Tuned were particularly effective, dominating the trade-offs between accuracy and both latency and energy consumption. This comparative analysis provides insights into optimizing neural network performance in resource-constrained environments.