Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
This addresses energy efficiency for deep learning practitioners in vision tasks, but it is incremental as it builds on existing network architectures with a conditional activation mechanism.
The paper tackles the problem of high computational and energy requirements in deep neural networks for vision applications by proposing Conditional Deep Learning (CDL), which dynamically adjusts computational effort based on input difficulty, resulting in a 1.91x reduction in operations and a 1.84x improvement in energy efficiency on the MNIST dataset, while increasing accuracy from 97.5% to 98.9%.
Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications. However, the computational and energy 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 require the full computational effort of a network, while a large majority can be classified correctly with very low effort. In this paper, 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 upon the difficulty of the input data while maintaining competitive classification accuracy. We evaluate our approach on the MNIST dataset. Our experiments demonstrate that our proposed CDL yields 1.91x reduction in average number of operations per input, which translates to 1.84x improvement in energy. In addition, our results show an improvement in classification accuracy from 97.5% to 98.9% as compared to the original network.