# Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets

arXiv: https://arxiv.org/abs/1610.02850 · HTML: https://mlsanity.com/paper/1610.02850/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2016.

## Built on by (most cited first, 4 of 4)

- [Anytime Dense Prediction with Confidence Adaptivity](https://mlsanity.com/paper/2104.00749/) (2021)
- [Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing](https://mlsanity.com/paper/2309.05686/) (2023)
- [Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks](https://mlsanity.com/paper/2403.07958/) (2024)
- [ItNet: iterative neural networks with small graphs for accurate, efficient and anytime semantic segmentation](https://mlsanity.com/paper/2101.08685/) (2021)

## Abstract (arXiv)

We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application. They allow for individual budgets given a priori for each test example and for anytime prediction, i.e., a possible interruption at multiple stages during inference while still providing output estimates. Our approach can therefore tackle the computational costs and energy demands of DNNs in an adaptive manner, a property essential for real-time applications. Our Impatient DNNs are based on a new general framework of learning dynamic budget predictors using risk minimization, which can be applied to current DNN architectures by adding early prediction and additional loss layers. A key aspect of our method is that all of the intermediate predictors are learned jointly. In experiments, we evaluate our approach for different budget distributions, architectures, and datasets. Our results show a significant gain in expected accuracy compared to common baselines.