message: Add clustering to TF Model Optimization overview
diff: @@ -33,8 +33,8 @@ models have the following benefits:
  translate to better performance and stability.
  Quantization can reduce the size of a model in all of these cases, potentially
  -at the expense of some accuracy. Pruning can reduce the size of a model for
  -download by making it more easily compressible.
  +at the expense of some accuracy. Pruning and clustering can reduce the size of
  +a model for download by making it more easily compressible.
  ### Latency reduction
  @@ -54,7 +54,7 @@ Some hardware accelerators, such as the
  with models that have been correctly optimized.
  Generally, these types of devices require models to be quantized in a specific
  -way. See each hardware accelerators documentation to learn more about their
  +way. See each hardware accelerator's documentation to learn more about their
  requirements.
  ## Trade-offs
  @@ -70,7 +70,7 @@ certain models may gain some accuracy as a result of the optimization process.
  ## Types of optimization
  -TensorFlow Lite currently supports optimization via quantization and pruning.
  +TensorFlow Lite currently supports optimization via quantization, pruning and clustering.
  These are part of the
  [TensorFlow Model Optimization Toolkit](https://www.tensorflow.org/model_optimization),
  @@ -134,6 +134,17 @@ technique for reducing model download size.
  In the future, TensorFlow Lite will provide latency reduction for pruned models.
  +### Clustering
  +
  +[Clustering](https://www.tensorflow.org/model_optimization/guide/clustering)
  +works by grouping the weights of each layer in a model into a predefined
  +number of clusters, then sharing the centroid values for the weights
  +belonging to each individual cluster. This reduces the number of unique weight
  +values in a model, thus reducing its complexity.
  +
  +As a result, clustered models can be compressed more effectively, providing
  +deployment benefits similar to pruning.
  +
  ## Development workflow
  As a starting point, check if the models in
  @@ -149,4 +160,4 @@ is the better option. See additional optimization techniques under the
  [TensorFlow Model Optimization Toolkit](https://www.tensorflow.org/model_optimization).
  If you want to further reduce your model size, you can try [pruning](#pruning)
  -prior to quantizing your models.
  +and/or [clustering](#clustering) prior to quantizing your models.
  \ No newline at end of file
  