NXP backend: Improve docs for NXP eIQ Neutron Kernel Selective Kernel… (#19772)

… Registration

### Summary
Docs improvement.

### Test plan
Docs only.


cc @robert-kalmar @JakeStevens @digantdesai @rascani

Author: roman-janik-nxp

docs/source/backends/nxp/nxp-kernel-selection.md

@@ -1,25 +1,25 @@
 # NXP eIQ Neutron Kernel Selective Kernel Registration
 
-The NXP ExecuTorch backend supports selective Neutron kernel registration for `Neutron-C` targets, which decreases the
+The NXP ExecuTorch backend supports selective Neutron kernel registration for `Neutron-C` targets, which reduces the
 size of the Neutron Firmware. During the backend's conversion to the Neutron representation by the Neutron Converter,
 microcode for the Neutron accelerator is generated.
 The microcode consists of kernel calls executed by the Neutron Driver. The code for kernel call functions is
-distributed in Neutron Firmware. 
+distributed in the Neutron Firmware. 
 
-The `eiq_neutron_sdk.neutron_converter` optionally generates the `*_kernel_selection.c` file, registering 
-only kernels that are required for a particular model or in the case of ExecuTorch, a delegated subgraph. This 
-`*_kernel_selection.c`, when used during the application linking, takes precedence over the default list of registered 
+The `eiq_neutron_sdk.neutron_converter` optionally generates a `*_kernel_selection.c` file, registering 
+only kernels that are required for a particular model or, in the case of ExecuTorch, a delegated subgraph. This 
+`*_kernel_selection.c`, when used during application linking, takes precedence over the default list of registered 
 kernels in the Neutron Firmware, and allows the linker to include only the necessary Neutron kernels.
-This software is required for deployment on an edge device (e.g. `i.MXRT700`) and is
-distributed via the MCUXpresso SDK. The MCUXpresso SDK enables building of a final application that is then flashed on 
+The Neutron Firmware is required for deployment on an edge device (e.g. `i.MX RT700`) and is
+distributed via the MCUXpresso SDK. The MCUXpresso SDK enables the building of a final application that is then flashed on 
 the edge device. For more details about this process, see
 [eIQ ExecuTorch Library User Guide](https://mcuxpresso.nxp.com/mcuxsdk/latest/html/middleware/eiq/executorch/docs/nxp/ugindex.html).
 
-By default, for Neutron-C targets like `i.MXRT700`, all kernel implementations are present in the Neutron Firmware, which
+By default, for Neutron-C targets like `i.MX RT700`, all kernel implementations are present in the Neutron Firmware, which
 is linked to the final application. This enables an easy build process for any model, but increases the size of the
-final application with unused code. In the case of limited RAM, you can link only kernels that are used in the set of
-models deployed. This way you can reduce the size of the final app by linking only selected kernels, used in one or
-multiple models.
+final application with unused code. In memory-constrained environments, you can link only the kernels required by the
+deployed models. This way you can reduce the size of the final application by linking only selected kernels, used in one
+or more models.
 
 The feature works as follows: The Neutron Converter with the appropriate flag exports a kernel selection file for each 
 converted subgraph, the kernel selection files are then merged and ready to be included in the MCUXpresso SDK to use for
@@ -30,7 +30,7 @@ a selection-only build.
 
 ## Export kernel selection file
 
-To turn on this feature on the side of NXP ExecuTorch backend, use the parameter `--dump_kernel_selection_code` in 
+To enable this feature in the NXP ExecuTorch backend, use the parameter `--dump_kernel_selection_code` in 
 `aot_neutron_compile.py`. An example with the CifarNet model:
 
 ```commandline
@@ -43,7 +43,7 @@ This command will create a `*_kernel_selection.c` file alongside the converted P
 
 ## Kernel Registration for Multiple Models
 
-If you want to use or experiment with multiple models in one application while having reduced kernel set, you can
+If you want to use or experiment with multiple models in one application while having a reduced kernel set, you can
 create one kernel selection file with the script `merge_kernel_selection_code.py`:
 
 ```commandline