Efficient Adverse-Weather Restoration for Unified-Memory Edge GPUs via Memory-Traffic-Aware Fusion

Adverse-weather image restoration is increasingly needed in edge vision systems, yet many recent methods are developed primarily for accuracy on server-class hardware rather than efficient deployment on resource-constrained platforms. This gap is particularly important for unified-memory edge GPUs, where memory traffic, activation movement, and latency variability can become major bottlenecks during inference. To address this issue, this paper presents an efficient adverse-weather restoration framework for unified-memory edge GPUs based on a memory-traffic-aware fusion strategy. Instead of relying on heavy multi-branch interaction or traffic-intensive feature aggregation, the proposed design emphasizes compact feature exchange, activation-aware computation, and hardware-friendly luminance modulation under constrained memory bandwidth. The framework is developed to preserve restoration quality while reducing unnecessary intermediate data movement, thereby improving runtime efficiency and practical deployability on edge devices. Experiments on ACDC show that the proposed MW-DSNet improves downstream semantic segmentation robustness to 49.8% mIoU under a fixed segmentation head, outperforming the no-restoration input by +6.9 points and TransWeather by +0.8 points. On the NVIDIA Jetson Orin Nano (NVIDIA Corporation, Santa Clara, CA, USA) under the 15 W mode, the FP16 TensorRT engine sustains 30.0 FPS at 720p with 35.1 ms p95 latency, 36.8 ms p99 latency, and 650 MB/frame DRAM traffic. INT8 deployment with night heavy calibration further improves throughput to 42.5 FPS and reduces DRAM traffic to 380 MB/frame while limiting the mIoU drop to 1.7 points. These measured results indicate that memory-traffic-aware fusion and luminance-conditioned modulation provide a practical accuracy–efficiency trade-off for unified-memory edge GPUs.