Why Run Deep Learning Locally?
Sending image, audio, or video data to cloud servers for deep learning processing has drawbacks: it consumes user bandwidth, introduces network latency, and exposes personal data to external systems. Running neural networks locally on mobile devices solves these issues.
Model Optimization Techniques
Raw deep learning models are too heavy for mobile batteries and memory. Engineers optimize them using:
- Quantization: Converting 32-bit floating-point weights (FP32) into 8-bit integers (INT8), reducing model size by 75% with minimal accuracy loss.
- Pruning: Removing neural connections that have close-to-zero weights.
- OS Acceleration: Utilizing Apple Neural Engine (CoreML) or Android Neural Networks API (NNAPI) to run models on dedicated hardware.