Add the ExecuTorch Overview section

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docs/source/index.rst

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 Welcome to the ExecuTorch Documentation
 =======================================
 
+.. important::
+   This is a preview version of ExecuTorch and should be used for testing and
+   evaluation purposes only. It is not recommended for use in production
+   settings. We welcome any feedback, suggestions, and bug reports from the
+   community to help us improve the technology. Please use the
+   `PyTorch Forums <https://discuss.pytorch.org/>`__ for discussion and feedback
+   about ExecuTorch using the tag **#executorch** and our
+   `GitHub repository <https://github.com/pytorch/executorch/issues>`__ for bug
+   reporting.
+
 **ExecuTorch** is a PyTorch platform that provides infrastructure to run
 PyTorch programs everywhere from AR/VR wearables to standard on-device
 iOS and Android mobile deployments. One of the main

docs/source/intro-overview.md

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 # ExecuTorch Overview
 
-TBA
+**ExecuTorch** is an end-to-end solution for enabling on-device inference
+capabilities across mobile and edge devices including wearables, embedded
+devices and microcontrollers. It is part of the PyTorch Edge ecosystem and
+enables efficient deployment of PyTorch models to edge devices.
+
+Key value propositions of ExecuTorch are:
+
+- **Portability:** Compatibility with a wide variety of computing platforms,
+  from high-end mobile phones to highly constrained embedded systems and
+  microcontrollers.
+- **Productivity:** Enabling developers to use the same toolchains and SDK from
+  PyTorch model authoring and conversion, to debugging and deployment to a wide
+  variety of platforms.
+- **Performance:** Providing end users with a seamless and high-performance
+  experience due to a lightweight runtime and utilizing full hardware
+  capabilities such as CPUs, NPUs, and DSPs.
+
+## Why ExecuTorch?
+
+Supporting on-device AI presents unique challenges with diverse hardware,
+critical power requirements, low/no internet connectivity, and realtime
+processing needs. These constraints have historically prevented or slowed down
+the creation of scalable and performant on-device AI solutions. We designed
+ExecuTorch, backed by our industry partners like Meta, Arm, Apple, and Qualcomm,
+to be highly portable and provide superior developer productivity without losing
+on performance.
+
+## How is ExecuTorch Different from PyTorch Mobile (Lite Interpreter)?
+
+PyTorch Mobile uses [TorchScript](https://pytorch.org/docs/stable/jit.html) to
+allow PyTorch models to run on devices with limited resources. ExecuTorch has a
+significantly smaller memory size and a dynamic memory footprint resulting in
+superior performance compared to PyTorch Mobile. Also, ExecuTorch does not rely
+on TorchScript, and instead leverages PyTorch 2 compiler and export
+functionality for on-device execution of PyTorch models.
+
+Read more in-depth technical overview topics about ExecuTorch:
+
+- [How ExecuTorch Works](intro-how-it-works.md)
+- [High-level Architecture and Components of ExecuTorch](getting-started-architecture.md)
+- [ExecuTorch Runtime Overview](runtime-overview.md)