Add quantization overview page.

PiperOrigin-RevId: 235995881

tensorflow/lite/g3doc/convert/quantization.md

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+# Converting Quantized Models
+
+This page provides information for how to convert quantized TensorFlow Lite
+models. For more details, please see the
+[model optimization](../performance/model_optimization.md).
+
+# Post-training: Quantizing models for CPU model size
+
+The simplest way to create a small model is to quantize the weights to 8 bits
+and quantize the inputs/activations "on-the-fly", during inference. This
+has latency benefits, but prioritizes size reduction.
+
+During conversion, set the `optimizations` flag to optimize for size:
+
+```
+converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
+converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
+tflite_quant_model = converter.convert()
+```
+
+# During training: Quantizing models for integer-only execution.
+
+Quantizing models for integer-only execution gets a model with even faster
+latency, smaller size, and integer-only accelerators compatible model.
+Currently, this requires training a model with
+["fake-quantization" nodes](https://github.com/tensorflow/tensorflow/tree/r1.13/tensorflow/contrib/quantize).
+
+Convert the graph:
+
+```
+with tf.Session() as sess:
+  converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
+  converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
+  input_arrays = converter.get_input_arrays()
+  converter.quantized_input_stats = {input_arrays[0] : (0., 1.)}  # mean, std_dev
+  tflite_model = converter.convert()
+```
+
+For fully integer models, the inputs are uint8. The `mean` and `std_dev values`
+specify how those uint8 values map to the float input values used while training
+the model.
+
+`mean` is the integer value from 0 to 255 that maps to floating point 0.0f.
+`std_dev` is 255 / (float_max - float_min)
+
+For most users, we recommend using post-training quantization. We are working on
+new tools for post-training and during training quantization that we hope will
+simplify generating quantized models.