RL-Agent-based Early-Exit DNN Architecture Search Framework

This paper introduces a Reinforcement Learning (RL)-based framework for optimizing early-exit configurations in Deep Neural Networks (DNNs). By integrating RL with BranchyNet-inspired architectures, the framework dynamically determines optimal early exit placements and confidence thresholds, balancing inference time, energy consumption, and accuracy. Key contributions include an early-exit DNN architecture search, an RL-driven threshold optimization process during training, and a design-space exploration open-source framework. Experiments on models such as ResNet-18, VGG-16, and AlexNet, using benchmarks like CIFAR-10 and MNIST, reveal significant reductions in inference time (up to 69.7x) and power consumption while keeping accuracy drop within 1-2%. This work demonstrates that dynamic early-exit strategies can enhance DNN efficiency while maintaining performance, paving the way for resource-constrained applications.