Mobile AI Deployment Framework

A robust framework for deploying AI models on mobile devices with hardware acceleration support for MediaTek and Qualcomm platforms.

Features

• Hardware acceleration support for:
  • MediaTek APU (via NeuroPilot SDK)
  • Qualcomm Hexagon DSP (via Snapdragon Neural Processing SDK)
  • Fallback to ARM CPU and GPU
• Multiple model format support:
  • TensorFlow Lite
  • PyTorch Mobile
  • ONNX Runtime
• Efficient memory and power management
• Android HAL integration
• Security features with Android Keystore

Requirements

• Android SDK 21+ (Android 5.0 or higher)
• Android NDK r21+
• CMake 3.18.1+
• Hardware-specific SDKs:
  • MediaTek NeuroPilot SDK
  • Qualcomm Neural Processing SDK

Project Structure

mobile-ai/
├── app/
│   ├── src/
│   │   ├── main/
│   │   │   ├── java/
│   │   │   │   └── com/
│   │   │   │       └── mobileai/
│   │   │   │           ├── core/
│   │   │   │           ├── hardware/
│   │   │   │           └── inference/
│   │   │   └─�� cpp/
│   │   │       ├── hardware/
│   │   │       ├── inference/
│   │   │       └── core/
│   │   └── test/
│   └── build.gradle
├── libs/
├── docs/
├── examples/
└── benchmarks/

Getting Started

1. Clone the repository:

git clone https://github.com/yourusername/mobile-ai.git

1. Install dependencies:

• Download and install Android Studio
• Install the Android NDK and CMake through Android Studio's SDK Manager
• Download the required hardware-specific SDKs from MediaTek and Qualcomm

1. Build the project:

./gradlew build

Usage

Basic Integration

// Initialize hardware manager
val hardwareManager = HardwareManager()
hardwareManager.initialize()

// Create model manager
val modelManager = ModelManager(hardwareManager)

// Load and run model
modelManager.loadModel("model.tflite", ModelFormat.TFLITE)
val input = floatArrayOf(/* your input data */)
val output = modelManager.runInference(input)

Hardware Acceleration

// Check available accelerators
val accelerators = hardwareManager.getAvailableAccelerators()
println("Available accelerators: $accelerators")

// Set preferred accelerator
hardwareManager.setPreferredAccelerator(HardwareManager.ACCELERATOR_MTK)

Performance Optimization

1. Model Optimization:

   • Use quantization to reduce model size
   • Apply pruning to remove unnecessary weights
   • Convert to hardware-specific formats when possible

2. Hardware Acceleration:

   • Prioritize hardware accelerators over CPU
   • Use batch processing when appropriate
   • Implement proper power management

3. Memory Management:

   • Implement model caching
   • Use memory-mapped files for large models
   • Release resources when not in use

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• MediaTek for NeuroPilot SDK
• Qualcomm for Neural Processing SDK
• TensorFlow, PyTorch, and ONNX Runtime teams