Accurate sea ice mapping is essential for safe maritime navigation in polar regions, where rapidly changing ice conditions require timely and reliable information. While Sentinel-1 Synthetic Aperture Radar (SAR) provides high-resolution, all-weather observations of sea ice, conventional ground-based processing is limited by downlink bandwidth, latency, and energy costs associated with transmitting large volumes of raw data. On-board processing, enabled by dedicated inference chips integrated directly within the satellite payload, offers a transformative alternative by generating actionable sea ice products in orbit. In this context, we present TinyIceNet, a compact semantic segmentation network co-designed for on-board Stage of Development (SOD) mapping from dual-polarized Sentinel-1 SAR imagery under strict hardware and power constraints. Trained on the AI4Arctic dataset, TinyIceNet combines SAR-aware architectural simplifications with low-precision quantization to balance accuracy and efficiency. The model is synthesized using High-Level Synthesis and deployed on a Xilinx Zynq UltraScale+ FPGA platform, demonstrating near-real-time inference with significantly reduced energy consumption. Experimental results show that TinyIceNet achieves 75.216% F1 score on SOD segmentation while reducing energy consumption by 2x compared to full-precision GPU baselines, underscoring the potential of chip-level hardware-algorithm co-design for future spaceborne and edge AI systems.

翻译：精确的海冰测绘对于极地海域的安全航行至关重要，该地区冰情快速变化，需要及时可靠的信息。虽然哨兵-1号合成孔径雷达（SAR）能够提供高分辨率、全天候的海冰观测，但传统的地面处理方式受限于下行链路带宽、延迟以及与传输大量原始数据相关的能耗成本。通过将专用推理芯片直接集成在卫星载荷内实现的星上处理，通过在轨生成可操作的海冰产品，提供了一种变革性的替代方案。在此背景下，我们提出了TinyIceNet，这是一种紧凑的语义分割网络，专为在严格的硬件和功耗约束下，从双极化哨兵-1号SAR图像进行星载发展阶段（SOD）测绘而协同设计。基于AI4Arctic数据集训练的TinyIceNet，结合了SAR感知的架构简化与低精度量化，以平衡准确性与效率。该模型使用高层次综合工具进行综合，并部署在Xilinx Zynq UltraScale+ FPGA平台上，实现了近实时推理，同时显著降低了能耗。实验结果表明，TinyIceNet在SOD分割任务上达到了75.216%的F1分数，同时与全精度GPU基线相比能耗降低了2倍，凸显了芯片级硬件-算法协同设计对未来星载及边缘AI系统的潜力。