message: Added some information about using uint8 data in TF2
  Reviewers: #tensorflow, #framework_ip_review_-_any_oss_or_third-party_code_use_has_been_approved, jamiep, zigmasb
  Subscribers: zigmasb, grahamh, alfiee, georgep, jamiep
  Maniphest Tasks: T39791
diff: @@ -3,7 +3,7 @@ Efficient IPU I/O
  When developing applications for the IPU, maximising the I/O performance is
  important. If an application is I/O-bound, after optimisation of the host data
  loading, then you can explore further optimisations of the movement of data
  -into the IPU. This chapter will cover two options that can improve I/O
  +into the IPU. This chapter will cover three options that can improve I/O
  performance.
  Prefetch elements
  @@ -82,3 +82,18 @@ very large number of I/O tiles can cause performance regressions in the main
  computation. However, too few I/O tiles can cause the transferred tensors to
  not fit in the available tile memory. Therefore, this may require some
  experimentation to find the best value for a specific application.
  +
  +uint8 data
  +~~~~~~~~~~
  +Using ``uint8`` data, or converting existing data to ``uint8`` before
  +training, enables increased bandwidth and lower memory usage at the cost of
  +decreased precision.
  +
  +We do not support doing calculations directly in ``uint8``, however in many
  +cases TensorFlow will implicitly cast ``uint8`` tensors to an appropriate data
  +type. This means that often you can use ``uint8`` data without requiring any
  +changes to your model.
  +
  +In some cases you will need to manually insert cast operations at the beginning
  +of the model. This can be done using ``keras.backend.cast``. For ``Sequential``
  +models, this can be wrapped in a ``keras.layers.Lambda`` layer.
  