Google Pixel Camera AI Pipeline
Edge AI deployment: mobile and IoT constraints, model compression, offline capability, and distributed edge inference systems.
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Designing edge AI deployment systems for mobile devices and IoT with model compression, offline capability, privacy-preserving inference, and distributed edge computing
📱 10M+ Mobile Devices🔋 Battery Optimized🔒 Privacy-First📡 Offline Capable
✅ Functional Requirements
- • Real-time inference on mobile devices (iOS/Android) and IoT hardware
- • Offline capability with local model execution and data processing
- • Over-the-air model updates with incremental deployment
- • Multi-model support for different use cases and device capabilities
- • Privacy-preserving inference with on-device data processing
- • Adaptive model selection based on device resources and network
- • Battery-optimized inference with power management
- • Cross-platform compatibility and consistent user experience
⚡ Non-Functional Requirements
- • Inference latency <50ms on mobile CPUs for critical applications
- • Model size <50MB for mobile deployment, <10MB for IoT devices
- • Memory footprint <200MB during inference on mobile devices
- • Battery drain <5% per hour for continuous AI workloads
- • Support 1000+ device types with varying compute capabilities
- • Network bandwidth usage <1MB for model updates on cellular
- • 99.9% offline availability with graceful degradation
- • Deployment to 10M+ devices with staged rollout capabilities
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