This paper introduces self-optimizing neural network architectures that adapt dynamically during inference for enhanced accuracy.
Traditional neural networks lack the ability to adapt in real-time to changing data distributions. Our research introduces a framework where networks self-modify their architecture during inference to maintain peak performance with dynamic workloads.
Download Full Paper (PDF)Networks modify their layer depth and complexity in response to input patterns without retraining.
Automated optimization of computation resources during inference for energy efficiency.
Efficiency boost in real-time inference
Reduction in energy consumption
Faster training to production cycle
Neural layers dynamically expand or shrink based on task complexity detected in input data.
Continuous adaptation using lightweight training mechanisms triggered during inference.