End-to-end pipeline for deploying deep learning models on edge devices: model conversion, quantization, hardware acceleration, and Android integration.
This project demonstrates the complete workflow of taking a trained deep learning model and optimizing it for on-device inference, including model conversion, quantization, hardware acceleration, and mobile integration.
The implementation mirrors real-world mobile AI deployment pipelines used in production systems.
As AI moves beyond the cloud, running models directly on devices enables:
- Lower latency inference
- Improved privacy and security
- Reduced dependency on cloud infrastructure
- Real-time intelligent applications
This repository demonstrates how to deploy a real-time image segmentation model on device, optimize it for mobile hardware, and evaluate its performance.
This project implements the complete edge AI deployment workflow.
- Load pretrained deep learning models
- Export models from PyTorch / TensorFlow
- Prepare neural network computation graphs for device deployment
- Apply post-training quantization
- Reduce model size and improve inference speed
- Maintain numerical accuracy while optimizing performance
- Convert models into device-compatible runtime formats
- Prepare models for execution on mobile hardware
Evaluate inference performance using different compute units:
- CPU
- GPU
- NPU / AI accelerators
- Benchmark inference latency
- Compare performance before and after quantization
- Validate numerical accuracy after deployment
Demonstration of integrating an optimized segmentation model into an Android application for real-time inference.
- Python
- PyTorch
- TensorFlow
- Model Quantization
- Edge AI runtimes
- Android integration
- Hardware acceleration (CPU / GPU / NPU)
Several optimization techniques were applied to make the model suitable for edge devices.
| Optimization | Impact |
|---|---|
| Quantization | Reduced model size |
| Graph capture and compilation | Improved runtime efficiency |
| Hardware acceleration | Lower latency inference |
Quantization can achieve up to 4× smaller model size and 4× faster inference, depending on device hardware.