Enabling Efficient Portrait Segmentation on Embedded Devices

Portrait segmentation (isolating a picture’s subject from the background) has become a key step for applicationssuch 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, reducing system complexity and power consumption. In this work, we present SINergy, a scalable and hardwareaware 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. Our experimental results show that SINergy achieves 2× to 5× speedup over existing architectures while preserving accuracy, and delivers the first real-time implementation of portrait segmentation on microcontrollers. Beyond tiny devices, the same hardware-aware principles yield over 80% latency reduction on GPU-equipped platforms. On a Greenwaves GAP9 microcontroller, SINergy requires just 6.4 ms for segmentation under 60 mW of power. These results establish SINergy as a scalable and energy-efficient solution, enabling practical deployment of portrait segmentation across the full spectrum of embedded computing. Code is available athttps://github.com/RickyBenevelli/SINergy