message: Documentation on the new experimental option for 16x8.
diff: @@ -151,6 +151,49 @@ The disadvantages of float16 quantization are as follows:
  to float32 when run on the CPU. (Note that the GPU delegate will not perform
  this dequantization, since it can operate on float16 data.)
  +### Integer only: 16-bit activations with 8-bit weights (experimental)
  +
  +This is an experimental quantization scheme. It is similar to the "integer only"
  +scheme, but activations are quantized based on their range to 16-bits, weights are
  +quantized in 8-bit integer and bias is quantized into 64-bit integer.
  +This is referred to as 16x8 quantization further.
  +
  +The main advantage of this quantization is that it can improve accuracy
  +significantly, but only slightly increase model size.
  +
  +<pre>
  +import tensorflow as tf
  +converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  +<b>converter.optimizations = [tf.lite.Optimize.DEFAULT]
  +converter.target_spec.supported_types = [tf.lite.constants.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8]</b>
  +tflite_quant_model = converter.convert()
  +</pre>
  +
  +If 16x8 quantization is not supported for some operators in the model,
  +then the model still can be quantized, but unsupported operators kept in float.
  +The following option should be added to the target_spec to allow this.
  +<pre>
  +import tensorflow as tf
  +converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  +converter.optimizations = [tf.lite.Optimize.DEFAULT]
  +converter.target_spec.supported_types = [tf.lite.constants.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8,
  +<b>tf.lite.OpsSet.TFLITE_BUILTINS</b>]
  +tflite_quant_model = converter.convert()
  +</pre>
  +
  +Examples of the use cases where accuracy improvements provided by this quantization scheme include:
  +* super-resolution,
  +* audio signal processing such as noise cancelling and beamforming,
  +* image de-noising,
  +* HDR reconstruction from a single image.
  +
  +The disadvantage of this quantization is:
  +
  +* Currently inference is noticeably slower than 8-bit full integer due to the lack of optimized kernel implementation.
  +* Currently it is incompatible with the existing hardware accelerated TFLite delegates.
  +
  +Note: This is an experimental feature.
  +
  ### Model accuracy
  Since weights are quantized post training, there could be an accuracy loss,
  