AI 中文总结
研究针对超低功耗MCU深度估计问题，提出结合多模态传感器融合与轻量级基于循环Transformer架构的自适应方法，通过令牌传播和增量传感器利用机制平衡能耗与精度，实验证明该方法节能且精度提升显著。
AI 中文摘要
基于人工智能的多模态传感器融合在超低功耗嵌入式和网络物理系统中愈发重要，它能提升可靠性、准确性和鲁棒性。但在低于100mW的平台上增加传感器需平衡能耗与预测精度。为此提出一种新颖的自适应人工智能方法，将多模态传感器融合与轻量级基于循环Transformer的架构结合。通过迭代中的令牌传播和增量传感器利用机制解决深度图估计任务。设计了包含所有三个传感器的新型印刷电路板及相关芯片进行算法部署和测试。实验表明，自适应系统在NYUv2数据集上与使用所有传感器和迭代的相同管道相比，在精度损失仅4.8%的情况下实现了90%的节能；与MobileDepth相比，尽管参数少9倍，但{\delta}1精度仅低5.6%；与运行在GAP9上的最先进模型相比，在相同平均功率预算下，自适应传感器融合使{\delta}1精度提高了31.8%。
英文摘要
Artificial intelligence (AI)-based multimodal sensor fusion is a relevant topic gaining ever more traction across ultra-low-power (ULP) embedded and cyber-physical systems, as it improves reliability, accuracy, and robustness under real-world constraints. However, adding more and more sensors to ultra-constrained sub-100 mW platforms requires balancing energy consumption against prediction accuracy. To achieve this ambitious goal, we present a novel adaptive AI methodology that combines multimodal sensor fusion (camera, ultrasound, and Time-of-Flight sensors) with a lightweight recurrent Transformer-based architecture (688 k parameters). We address the depth map estimation task with a mechanism that combines token propagation across iterations with incremental sensor utilization. At each iteration, a confidence-based gating mechanism dynamically decides whether to continue the computation by adding progressively richer but more power-demanding sensors as input. Token propagation ensures temporal consistency by forwarding context features across time. To deploy our algorithm and test a first real-world prototype, we design a novel printed circuit board featuring all three sensors, coupled with an ULP GWT GAP9 multicore System-on-Chip. When comparing our adaptive system against the same pipeline using all sensors and iterations on the NYUv2 dataset, we lose only 4.8% of the δ1 accuracy in exchange for 90% energy saving (2.44 mJ/frame). Finally, our adaptive method marks only 5.6% lower δ1 accuracy than MobileDepth despite using 9x fewer parameters. Compared with a state-of-the-art model also running on GAP9, our method improves δ1 accuracy by 31.8% thanks to our adaptive sensor fusion while operating within the same average power budget (~400 mW).
Comments16 pages, 9 figures, 6 tables. This paper has been accepted for publication in the IEEE Sensors Journal Copyright 2026 IEEE
