Enabling Efficient Portrait Segmentation on Embedded Devices

Portrait segmentation, isolating a picture’s subject from the background, has become a key step for applications such as video conferencing, augmented reality, and mobile imaging. Deploying these capabilities directly on low-power devices, including smartphones, action cameras, and even within image sensors themselves, eliminates reliance on external processors and reduces system complexity and power consumption. We present SINergy, a scalable and hardware-aware portrait segmentation network specifically designed for embedded deployment. SINergy builds upon efficient backbones such as XiNet and PhiNet, optimizing arithmetic intensity, memory access patterns, and operator compatibility rather than conventional FLOP-centric metrics. We systematically evaluate SINergy across heterogeneous platforms, ranging from consumer microcontrollers to small accelerators, single-board computers, and GPU-equipped edge devices. Experimental results show that SINergy achieves 2x to 5x speedup over existing architectures while preserving accuracy, and delivers the first real-time implementation of portrait segmentation on microcontrollers.