Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks

TL;DR

This paper proposes new decision mechanisms for early exit neural networks that leverage temporal correlation in sensor data, significantly reducing computation while maintaining high accuracy in embedded applications.

Contribution

It introduces Difference Detection and Temporal Patience as novel decision mechanisms that utilize temporal correlation for efficient early exit decisions in neural networks.

Findings

Reduced mean operations per inference by up to 80%.

Maintained accuracy within 5% of the original model.

Effective across health monitoring, image classification, and wake-word detection.

Abstract

Deep Learning is becoming increasingly relevant in Embedded and Internet-of-things applications. However, deploying models on embedded devices poses a challenge due to their resource limitations. This can impact the model's inference accuracy and latency. One potential solution are Early Exit Neural Networks, which adjust model depth dynamically through additional classifiers attached between their hidden layers. However, the real-time termination decision mechanism is critical for the system's efficiency, latency, and sustained accuracy. This paper introduces Difference Detection and Temporal Patience as decision mechanisms for Early Exit Neural Networks. They leverage the temporal correlation present in sensor data streams to efficiently terminate the inference. We evaluate their effectiveness in health monitoring, image classification, and wake-word detection tasks. Our novel contributions were able to reduce the computational footprint compared to established decision mechanisms significantly while maintaining higher accuracy scores. We achieved a reduction of mean operations per inference by up to 80% while maintaining accuracy levels within 5% of the original model. These findings highlight the importance of considering temporal correlation in sensor data to improve the termination decision.