With the increasing adoption of unmanned aerial vehicles (UAVs) in critical applications such as infrastructure inspection and emergency response, efficient on-site recognition via live video analytics and streaming has become essential. However, the inherent resource limitation poses significant challenges for performing simultaneous and real-time video analytics and streaming on UAVs. To address this issue, we propose a unified framework that orchestrate the Neural Processing Unit (NPU) and Graph Processing Unit (GPU) of the Systems-on-Chip (SoC) processor to accelerate and carefully schedule the pipeline of video analytics and streaming on UAVs. Additionally, our system incorporates frame interpolation to enable real-time streaming of video analytics results, providing immediate visual feedback to on-site operators. Empirical results on a commercial UAV equipped with Snapdragon 865 SoC platform show that our system reduces per-frame inference latency from 163ms (GPU) to 63ms (NPU), achieving a 2.6× speedup. Combined with optimized pre-processing and frame interpolation, our system increases effective streaming throughput from 2 to 30 FPS, enabling smooth and simultaneous real-time video analytics and streaming.

The growing demand for deploying Artificial Intelligence Generated Content (AIGC) models like Stable Diffusion on resource-constrained edge devices challenges balancing quality, lightweight implementation, and portability. The emergence of WebAssembly (WASM) offers a compactcross-platform, and isolated runtime environment, making it a promising solution for efficient edge AIGC inference. However, current WASM based AI inference solutions are restricted to text interactions, offering limited support for image generation. To solve the challenges, we propose WebAssembly-Rust based WasmSD-Edge, a lightweight, edge-oriented AI image generation framework for high performance on-device Stable Diffusion inference on various edge devices. WasmSD-Edge employs a plugin-based architecture by integrating stable-diffusion.cpp as a WASM backend plugin for the WasmEdge runtime. It exposes a set of WebAssembly System Interfaces (WASI) to support text-to-image, image-to-image, and model convertion. Additionally, a Rust Crate SDK further enables developers to parametrically control inference process and output generation. To evaluate usability and portability of WasmSD-Edge on heterogeneous devices, we deployed it on heterogeneous devices. It achieves high inference speed and image quality with low resource consumption, offering a practical and efficient solution for deploying edge AIGC workflow. The implementation has been merged into WasmEdge — one of the largest WASM community, and source code are available at: https://github.com/WasmEdge/wasmedge-stable-diffusion.