With the rapid advances of deep learning-based computer vision (CV) technology, digital images are increasingly processed not by humans, but by downstream CV algorithms. In particular, the growing popularity of vision foundation models has heightened interest in deploying these models on edge devices. However, limited memory remains a key bottleneck, making memory footprint reduction essential. Mainstream model customization methods often require intensive deployment efforts and can severely degrade accuracy. Moreover, existing deep learning frameworks generally do not prioritize memory optimization. Existing memory management schemes face practical limitations, including layer-wise memory imbalance, high management overhead, and volatile memory budgets.