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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