Quantization
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Quantization-aware model training
ensures that the forward pass matches precision for both training and inference. There are two aspects to this:
- Operator fusion at inference time are accurately modeled at training time.
- Quantization effects at inference are modeled at training time.
For efficient inference, TensorFlow combines batch normalization with the preceding convolutional and fully-connected layers prior to quantization by folding batch norm layers.
Post-training quantization
is a general technique to reduce the model size while also providing up to 3x lower latency with little degradation in model accuracy. Post-training quantization quantizes weights to 8-bits of precision from floating-point.
