DOCS shift to rst - devices and ARM (#16193)

docs/optimization_guide/dldt_deployment_optimization_guide.md

@@ -23,13 +23,24 @@ Therefore, optimization should start with defining the use case. For example, if
 
 It is also important to understand how the full-stack application would use the inference component "end-to-end." For example, to know what stages need to be orchestrated to save workload devoted to fetching and preparing input data.
 
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 For more information on this topic, see the following articles:
-* [feature support by device](@ref features_support_matrix)
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+@sphinxdirective
+* :ref:`feature support by device <devicesupport-feature-support-matrix>`
+@endsphinxdirective
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 * [Inputs Pre-processing with the OpenVINO](@ref inputs_pre_processing)
 * [Async API](@ref async_api)
 * [The 'get_tensor' Idiom](@ref tensor_idiom)
 * For variably-sized inputs, consider [dynamic shapes](../OV_Runtime_UG/ov_dynamic_shapes.md)
 
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 See the [latency](./dldt_deployment_optimization_latency.md) and [throughput](./dldt_deployment_optimization_tput.md) optimization guides, for **use-case-specific optimizations**
 
 ## Writing Performance-Portable Inference Applications

docs/optimization_guide/dldt_deployment_optimization_latency.md

@@ -22,7 +22,15 @@ Typically, human expertise is required to get more "throughput" out of the devic
 
 > **NOTE**: [OpenVINO performance hints](../OV_Runtime_UG/performance_hints.md) is a recommended way for performance configuration, which is both device-agnostic and future-proof.
 
-When multiple models are to be used simultaneously, consider running inference on separate devices for each of them. Finally, when multiple models are executed in parallel on a device, using additional `ov::hint::model_priority` may help to define relative priorities of the models. Refer to the documentation on the [matrix features support for OpenVINO devices](@ref features_support_matrix) to check if your device supports the feature.
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+@sphinxdirective
+* feature support by device 
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+When multiple models are to be used simultaneously, consider running inference on separate devices for each of them. Finally, when multiple models are executed in parallel on a device, using additional ``ov::hint::model_priority`` may help to define relative priorities of the models. Refer to the documentation on the :ref:`OpenVINO feature support for devices <devicesupport-feature-support-matrix>` to check if your device supports the feature.
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+@endsphinxdirective
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 **First-Inference Latency and Model Load/Compile Time**