A Guide to Quantization-Aware Training (QAT) for Android Model Deployment

Short summary

The article explains Quantization-Aware Training (QAT) as a solution to maintain model accuracy while reducing size and latency for Android deployment. It contrasts QAT with Post-Training Quantization (PTQ), detailing the math and fake quantization nodes used to train robust int8 models. The guide also highlights Google's shift to AICore and Gemini Nano for system-level model management and hardware orchestration on mobile devices.

• •QAT trains models to be robust to int8 quantization noise, avoiding the accuracy drop of PTQ.
• •Fake quantization nodes and the Straight-Through Estimator allow gradients to flow during training.
• •Google's AICore and Gemini Nano enable system-level model sharing and NPU orchestration on Android.

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