Move Apple runtime docs to new location

Author: GregoryComer

docs/source/index.rst

@@ -89,6 +89,7 @@ Topics in this section will help you get started with ExecuTorch.
    using-executorch-android
    using-executorch-ios
    using-executorch-cpp
+   using-executorch-runtime-integration
    using-executorch-troubleshooting
 
 .. toctree::

docs/source/using-executorch-android.md

@@ -1,3 +1,23 @@
 # Using ExecuTorch on Android
 
-Placeholder for top-level Android documentation
+To use from Android, ExecuTorch provides Java API bindings and Android platform integration, available as a AAR file. The ExecuTorch C++ APIs can also be used from Android native.
+
+## Installation
+
+TODO Instructions on downloading the pre-built AAR. Replace with Maven/Gradle package management when available.
+
+### Building from Source
+
+TODO Instructions on re-creating and customizing the Android AAR.
+
+## Android Backends
+
+TODO Describe commonly used backends, including XNN, Vulkan, and NPUs.
+
+## Runtime Integration
+
+TODO Code sample in Java
+
+## Next Steps
+
+TODO Link to Java API reference and other relevant material

docs/source/apple-runtime.md renamed to docs/source/using-executorch-ios.md

@@ -1,6 +1,8 @@
-# Integrating and Running ExecuTorch on Apple Platforms
+# Using ExecuTorch on iOS
 
-**Author:** [Anthony Shoumikhin](https://github.com/shoumikhin)
+ExecuTorch supports both iOS and macOS via Objective-C, Swift, and C++. ExecuTorch also provides backends to leverage Core ML and Metal Performance Shaders (MPS) for hardware-accelerated execution on Apple platforms.
+
+## Integration
 
 The ExecuTorch Runtime for iOS and macOS is distributed as a collection of prebuilt [.xcframework](https://developer.apple.com/documentation/xcode/creating-a-multi-platform-binary-framework-bundle) binary targets. These targets are compatible with both iOS and macOS devices and simulators and are available in both release and debug modes:
 
@@ -17,8 +19,6 @@ Link your binary with the ExecuTorch runtime and any backends or kernels used by
 
 **Note:** To access logs, link against the Debug build of the ExecuTorch runtime, i.e., the `executorch_debug` framework. For optimal performance, always link against the Release version of the deliverables (those without the `_debug` suffix), which have all logging overhead removed.
 
-## Integration
-
 ### Swift Package Manager
 
 The prebuilt ExecuTorch runtime, backend, and kernels are available as a [Swift PM](https://www.swift.org/documentation/package-manager/) package.
@@ -84,9 +84,9 @@ swift package resolve
 swift build
 ```
 
-### Local Build
+### Building from Source
 
-Another way to integrate the ExecuTorch runtime is to build the necessary components from sources locally and link against them. This route is more involved but certainly doable.
+Another way to integrate the ExecuTorch runtime is to build the necessary components from sources locally and link against them. This is useful when customizing the runtime.
 
 1. Install [Xcode](https://developer.apple.com/xcode/resources/) 15+ and Command Line Tools:
 
@@ -195,7 +195,7 @@ import ExecuTorch
 
 ### Logging
 
-We provide extra APIs for logging in Objective-C and Swift as a lightweight wrapper of the internal ExecuTorch machinery. To use it, just import the main framework header in Objective-C. Then use the `ExecuTorchLog` interface (or the `Log` class in Swift) to subscribe your own implementation of the `ExecuTorchLogSink` protocol (or `LogSink` in Swift) to listen to log events.
+ExecuTorch provides extra APIs for logging in Objective-C and Swift as a lightweight wrapper of the internal ExecuTorch machinery. To use it, just import the main framework header in Objective-C. Then use the `ExecuTorchLog` interface (or the `Log` class in Swift) to subscribe your own implementation of the `ExecuTorchLogSink` protocol (or `LogSink` in Swift) to listen to log events.
 
 ```objectivec
 #import <ExecuTorch/ExecuTorch.h>