Runtime-Robust Edge Inference System with Masking-Based Partial Update on Dynamic Reconfigurable FPGA

Edge inference systems must sustain real-time performance under dynamic environments such as sensor noise, illumination change, and new object classes. Conventional edge devices deploy static offline-trained models, causing accuracy degradation when the input distribution drifts. This study proposes a runtime-robust edge inference framework that enables continuous adaptation without interrupting execution. The edge device partitions its memory into active and adaptive regions, applying task-specific masked updates generated by a server-side FPGA. The FPGA performs layer-wise importance analysis, partial retraining, and adaptive mask generation using dynamic partial reconfiguration (DPR) to minimize reconfiguration delay. Experiments on MNIST, CIFAR-10, and Tiny ImageNet show that the proposed method reduces adaptation latency by up to 1.3× compared with GPU full retraining while cutting the communication cost to 28% of full model transmission. These results demonstrate that combining masking-based selective updates with FPGA DPR acceleration achieves real-time adaptability, low latency, and communication-efficient learning in cloud-edge collaborative environments.