Computer Vision Systems • Edge Deployment (MobileNet, EfficientNet-Lite)Easy⏱️ ~2 min
What Makes Edge Deployment Different from Cloud Inference?
Edge deployment runs computer vision models directly on the device where data is captured, whether that's a smartphone, IoT camera, or embedded system. Unlike cloud inference where you send images to powerful servers, edge inference must operate within severe constraints: power budgets measured in milliwatts, memory footprints under 50 MB, and thermal limits that cause throttling after seconds of continuous use.
The performance requirements are fundamentally different. A cloud server might handle batch requests with 200ms latency and consume watts of power per inference. An edge device running real time camera processing at 30 frames per second has only 33ms total budget per frame, including capture, preprocessing, inference, and postprocessing. Exceed that budget and your UI stutters or the system kills your app.
Consider Google's on device image classification in Android. The model must deliver results in 5 to 15ms on the Neural Processing Unit (NPU) at 224 by 224 resolution while keeping the entire app under a few hundred milliwatts for continuous operation. Tesla's in car perception systems face even stricter real time windows with safety critical requirements and zero tolerance for network dependency. The shared principle is simple: every millisecond and milliwatt counts, and you can't rely on external infrastructure.
💡 Key Takeaways
•Edge devices have a 33ms total frame budget for 30 fps real time processing, including all pipeline stages from capture to display
•Power consumption must stay under a few hundred milliwatts for continuous tasks to avoid rapid battery drain and thermal throttling
•Modern phones achieve 5 to 15ms inference at 224 by 224 on NPUs, but must handle device heterogeneity where low end devices run 8x slower
•Privacy and offline operation are critical benefits: on device processing avoids network round trips and keeps sensitive data local
•Thermal throttling can double latency from 12ms to 25ms after tens of seconds at full utilization, requiring adaptive workload management
📌 Examples
Google Android ML features use MobileNet derivatives to classify images in 5 to 15ms on device NPUs while keeping app power under 300mW for continuous camera tasks
Apple Core ML with Neural Engine maintains interactive latencies under 30ms for on device vision tasks across millions of deployed iPhones
Raspberry Pi 4 with Coral USB TPU runs SSD MobileNet V1 in 12ms at 0.10 mWh per inference, versus 209ms on CPU alone