Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks

The author introduces Difference Detection and Temporal Patience as decision mechanisms for Early Exit Neural Networks, leveraging temporal correlation to efficiently terminate inference while maintaining accuracy. The main thesis is to optimize the performance of EENNs within resource-constrained environments by considering temporal correlation in sensor data.