Adaptive Inference

Adaptive inference aims to optimize the efficiency of machine learning inference by dynamically adjusting model resources based on the characteristics of individual input data. Current research focuses on developing efficient algorithms and architectures, such as early-exit networks, cascaded ensembles, and dynamic sub-networks within transformers, to achieve this adaptation, often leveraging input-dependent routing or layer selection mechanisms. This approach holds significant promise for reducing computational costs, energy consumption, and latency in various applications, particularly in resource-constrained environments like edge devices and IoT platforms, while maintaining accuracy.