Dynamic Token Halting

Dynamic token halting is a technique that improves the efficiency of various computational models by selectively terminating less-contributing processing steps, thereby reducing computational cost without significant performance loss. Current research focuses on applying this to transformer-based architectures, including those used in natural language processing and 3D object detection, as well as recurrent neural networks, often employing differentiable halting mechanisms to enable end-to-end training. This approach offers significant potential for deploying complex models on resource-constrained devices, such as edge computing platforms, and for accelerating real-time applications requiring high throughput and low latency.