message: Mention Hexagon delegate in TFL delegate overview doc
diff: @@ -14,24 +14,26 @@ Running inference on compute-heavy machine learning models on mobile devices is
  Instead of relying on the CPU, some devices have hardware accelerators, such as GPU or DSP, that allows for better performance and higher energy efficiency.
  -## Using the GPU / NNAPI delegate
  +## Using the built-in delegates
  TensorFlow Lite provides the following delegates for hardware acceleration:
  * **GPU delegate for cross platform acceleration** - The GPU delegate can be
  used on both Android and iOS. It is optimized to run 32-bit and 16-bit float
  based models where a GPU is available. For an overview of the GPU delegate,
  - see
  - [TensorFlow Lite on GPU](gpu_advanced.md).
  - For step-by-step tutorials on using the GPU delegate with Android and iOS,
  - see
  + see [TensorFlow Lite on GPU](gpu_advanced.md). For step-by-step tutorials on
  + using the GPU delegate with Android and iOS, see
  [TensorFlow Lite GPU Delegate Tutorial](gpu.md).
  * **NNAPI delegate for newer Android devices** - The NNAPI delegate can be
  used to accelerate models on Android devices with GPU, DSP and / or NPU
  available. It is available in Android 8.1 (API 27+) or higher. For an
  overview of the NNAPI delegate, step-by-step instructions and best
  - practices, see
  - [TensorFlow Lite NNAPI delegate](nnapi.md).
  + practices, see [TensorFlow Lite NNAPI delegate](nnapi.md).
  +* **Hexagon delegate for older Android devices** - The Hexagon delegate can be
  + used to accelerate models on Android devices with Qualcomm Hexagon DSP. It
  + can be used on devices older version of Android OS that does not fully
  + support NNAPI. See [TensorFlow Lite Hexagon delegate](hexagon_delegate.md)
  + for more detail.
  ## How do delegates work?
  