Abstract
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.
Abstract (translated)
基于RISC-V的架构正在为智能边缘设备上的高效在设备学习（On-Device Learning，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，由于计算资源受限和设备寿命有限等原因，在电池供电的嵌入式平台上开发支持ODL的功能面临着重大挑战，同时还存在诸如灾难性遗忘等内在学习问题。为应对这些挑战，我们提出了一种基于正则化的在设备联合持续学习算法，专门针对执行面部识别任务的多个纳米无人机。我们在一个RISC-V架构的10核心超低功耗片上系统（SoC）上展示了我们的方法，并优化了ODL的计算需求。与简单的微调相比，我们通过这种方法提高了24%的分类准确性，每个本地周期需要178毫秒，而全球周期则需要10.5秒，这证明了该架构在此任务上的有效性。
URL
https://arxiv.org/abs/2503.17436
