Energy-Efficient and Improved Image Recognition with Conditional Deep Learning

Abstract

Deep-learning neural networks have proven to be very successful for a wide range of recognition tasks across modern computing platforms. However, the computational requirements associated with such deep nets can be quite high, and hence their energy-efficient implementation is of great interest. Although, traditionally, the entire network is utilized for the recognition of all inputs, we observe that the classification difficulty varies widely across inputs in real-world datasets; only a small fraction of inputs requires the full computational effort of a network, while a large majority can be classified correctly with very low effort. In this article, we propose Conditional Deep Learning (CDL), where the convolutional layer features are used to identify the variability in the difficulty of input instances and conditionally activate the deeper layers of the network. We achieve this by cascading a linear network of output neurons for each convolutional layer and monitoring the output of the linear network to decide whether classification can be terminated at the current stage or not. The proposed methodology thus enables the network to dynamically adjust the computational effort depending on the difficulty of the input data while maintaining competitive classification accuracy. The overall energy benefits for MNIST/CIFAR10/Tiny ImageNet datasets with state-of-the-art deep-learning architectures are 1.84 × /2.83 × /4.02 × , respectively. We further employ the conditional approach to train deep-learning networks from scratch with integrated supervision from the additional output neurons appended at the intermediate convolutional layers. Our proposed integrated CDL training leads to an improvement in the gradient convergence behavior giving substantial error rate reduction on MNIST/CIFAR-10, resulting in improved classification over state-of-the-art baseline networks.