Recent advances in deep learning, whether on discriminative or generative tasks have been beneficial for various applications, among which security and defense. However, their increasing computational demands during training and deployment translates directly into high energy consumption. As a consequence, this induces a heavy carbon footprint which hinders their widespread use and scalability, but also a limitation when deployed on resource-constrained edge devices for real-time use. In this paper, we briefly survey efficient deep learning methods for biometric applications. Specifically, we tackle the challenges one might incur when training and deploying deep learning approaches, and provide a taxonomy of the various efficient deep learning families. Additionally, we discuss complementary metrics for evaluating the efficiency of these models such as memory, computation, latency, throughput, and advocate for universal and reproducible metrics for better comparison. Last, we give future research directions to consider.

翻译：近年来，深度学习在判别式与生成式任务上的进展，已为包括安全与国防在内的诸多应用领域带来助益。然而，其在训练与部署阶段日益增长的计算需求直接导致了高能耗。其结果是，这产生了沉重的碳足迹，不仅阻碍了其广泛使用与可扩展性，也限制了其在资源受限的边缘设备上进行实时部署。本文简要综述了面向生物识别应用的高效深度学习方法。具体而言，我们探讨了在训练与部署深度学习模型时可能遇到的挑战，并对各类高效深度学习技术家族进行了分类梳理。此外，我们讨论了评估这些模型效率的补充性指标，如内存占用、计算量、延迟、吞吐量，并倡导建立通用且可复现的指标以促进更佳的比较。最后，我们提出了值得关注的未来研究方向。