高效深度学习助力生物识别，降低算力消耗与碳足迹。
Efficient Deep Learning for Biometrics: Overview, Challenges and Trends in Ear of Frugal AI
- 提出生物识别中高效深度学习方法的分类体系。
- 强调计算、内存、延迟等多维度效率评估的重要性。
- 适合关注边缘部署与绿色AI的研究者参考。
近年来，深度学习在判别与生成任务中的进展推动了安全与国防等领域的应用，但其训练与部署所需的日益增长的计算资源直接导致高能耗，带来沉重碳足迹，限制了大规模应用和在资源受限边缘设备上的实时部署。本文综述了生物识别应用中的高效深度学习方法，针对训练与部署面临的挑战，提出多种高效深度学习方法的分类体系，并讨论了内存、计算量、延迟、吞吐量等互补性评估指标，倡导采用统一且可复现的度量标准以促进模型比较。最后，展望了未来研究方向。
原文摘要 · Abstract (English)
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.
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