Binary Neural Networks: A Survey
This is an incremental survey paper that synthesizes existing research on binary neural networks for practitioners and researchers in efficient deep learning.
This paper surveys binary neural networks, which reduce storage and computation for deployment on resource-limited devices but face challenges like information loss and optimization difficulties due to binarization. It categorizes existing algorithms, evaluates their performance on tasks such as image classification, and discusses future research challenges.
The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected.