提出轻量级提示框架，让边缘设备持续学习更快更省电。
Critical Patch-Aware Sparse Prompting with Decoupled Training for Continual Learning on the Edge
- 按关键区域采样令牌，减少训练所需内存和计算
- 分离提示与分类器训练，降低反向传播开销
- 在真实边缘设备上实测效率提升1.6倍，精度接近顶尖水平
边缘设备上的持续学习需兼顾高精度与训练效率，以应对严苛的内存与算力限制。尽管基于提示的持续学习（PCL）参数高效且精度优异，但以往研究多关注精度或推理性能，忽视了设备端训练的内存与计算成本。本文提出CPS-Prompt框架，通过关键区域采样（CPS）实现任务感知的令牌压缩，并采用解耦提示与分类器训练（DPCT）策略，显著降低反向传播开销。在三个公开基准及真实边缘硬件上的实验表明，相比平衡版CODA-Prompt基线，CPS-Prompt在峰值内存、训练时间与能耗方面均提升约1.6倍，平均精度仅比当前最优的C-Prompt低2%，且在精度上仍可媲美CODA-Prompt。代码已开源。
原文摘要 · Abstract (English)
Continual learning (CL) on edge devices requires not only high accuracy but also training-time efficiency to support on-device adaptation under strict memory and computational constraints. While prompt-based continual learning (PCL) is parameter-efficient and achieves competitive accuracy, prior work has focused mainly on accuracy or inference-time performance, often overlooking the memory and computational costs of on-device training. In this paper, we propose CPS-Prompt, a critical patch-aware sparse prompting framework that explicitly targets training-time memory usage and computational cost by integrating critical patch sampling (CPS) for task-aware token reduction and decoupled prompt and classifier training (DPCT) to reduce backpropagation overhead. Experiments on three public benchmarks and real edge hardware show that CPS-Prompt improves peak memory, training time, and energy efficiency by about 1.6x over the balanced CODA-Prompt baseline, while maintaining accuracy within 2% of the state-of-the-art C-Prompt on average and remaining competitive with CODA-Prompt in accuracy. The code is available at https://github.com/laymond1/cps-prompt.
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