基于RISC-V的超低功耗SoC上的设备端联邦持续学习，用于智能纳米无人机群
On-Device Federated Continual Learning on RISC-V-based Ultra-Low-Power SoC for Intelligent Nano-Drone Swarms
AI总结：
本文提出一种基于正则化的设备端联邦持续学习算法，在RISC-V超低功耗SoC上实现纳米无人机群的人脸识别，较朴素微调提升24%准确率。
摘要：
基于RISC-V的架构正在为智能边缘设备中的高效设备端学习（ODL）铺平道路。当应用于多个节点时，ODL能够创建保护数据隐私的智能传感器网络。然而，开发支持ODL的电池供电嵌入式平台面临着重大挑战，原因在于计算资源受限和设备寿命有限，此外还存在诸如灾难性遗忘等内在学习问题。我们通过提出一种基于正则化的设备端联邦持续学习算法来应对这些挑战，该算法专为执行人脸识别任务的多个纳米无人机设计。我们在基于RISC-V的10核超低功耗SoC上展示了我们的方法，优化了ODL的计算需求。与朴素微调相比，我们将分类准确率提高了24%，每个本地epoch需178毫秒，每个全局epoch需10.5秒，证明了该架构在此任务上的有效性。
英文摘要：
RISC-V-based architectures are paving the way for efficient On-Device Learning (ODL) in smart edge devices. When applied across multiple nodes, ODL enables the creation of intelligent sensor networks that preserve data privacy. However, developing ODL-capable, battery-operated embedded platforms presents significant challenges due to constrained computational resources and limited device lifetime, besides intrinsic learning issues such as catastrophic forgetting. We face these challenges by proposing a regularization-based On-Device Federated Continual Learning algorithm tailored for multiple nano-drones performing face recognition tasks. We demonstrate our approach on a RISC-V-based 10-core ultra-low-power SoC, optimizing the ODL computational requirements. We improve the classification accuracy by 24% over naive fine-tuning, requiring 178 ms per local epoch and 10.5 s per global epoch, demonstrating the effectiveness of the architecture for this task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
