Multi-Objective Design Optimization for Image Classification Using Elastic Neural Networks

Image classification is an essential challenge for many types of autonomous and smart systems. With advances in Convolutional Neural Networks (CNNs), the accuracy of image classification systems has been dramatically improved. However, due to the escalating complexity of state-of-the-art CNN solutions, significant challenges arise in implementing real-time image classification applications on resource-constrained platforms. The framework of elastic neural networks has been proposed to address trade-offs between classification accuracy and real-time performance by leveraging intermediate early-exits placed in deep CNNs and allowing systems to switch among multiple candidate outputs, while switching off inference layers that are not used by the selected output. In this paper, we propose a novel approach for configuring early-exit points when converting a deep CNN into an elastic neural network. The proposed approach is designed to systematically optimize the quality and diversity of the alternative CNN operating points that are provided by the derived elastic networks. We demonstrate the utility of the proposed elastic neural network approach on the CIFAR-100 dataset.