Mobile Machine Learning
Running ML models efficiently on mobile devices.
Overview
Mobile Machine Learning is a research project focused on deploying and running machine learning models efficiently on mobile devices. The work explores how model architecture, hardware acceleration, and runtime constraints influence performance, battery consumption, and user experience.
The project treats mobile execution as a first-class constraint rather than a post-training optimization step.
Research Motivation
While modern ML models continue to grow in size and complexity, mobile devices impose strict limits on memory, compute, and energy usage. Bridging this gap requires careful design choices across the entire ML lifecycle.
This project investigates questions such as:
- How can models be adapted to run within mobile constraints?
- What trade-offs exist between accuracy, latency, and energy consumption?
- How does on-device inference impact privacy and responsiveness?
The goal is to enable practical ML capabilities without relying on constant cloud connectivity.
Optimization Techniques
The research explored multiple optimization strategies:
Model Quantization Reducing precision to lower memory usage and improve inference speed.
Architecture Simplification Evaluating lightweight architectures designed for mobile execution.
Hardware Acceleration Leveraging device-specific accelerators via Core ML and TensorFlow Lite.
Pipeline Optimization Minimizing data movement and preprocessing overhead.
Each technique was evaluated independently and in combination.
Experiments
Experiments were conducted on a range of iOS devices, focusing on:
- Inference latency under real-world conditions
- Memory footprint and startup time
- Battery impact during sustained usage
Results showed that carefully tuned models could achieve near real-time performance with minimal impact on device resources.
Outcomes
The project resulted in:
- A set of guidelines for mobile-first ML deployment
- Benchmark results across devices and frameworks
- Reference implementations using Core ML and TensorFlow Lite
The research phase is complete, and its findings have informed subsequent mobile and edge ML initiatives.