QA fixes for "kernel-library-overview"

Summary: The current link is a deprecated folder. Should link to the new one.

Reviewed By: dbort

Differential Revision: D50315054

fbshipit-source-id: 50da8ab9347d754cbbbb0b85270906e78bb0a38a

Author: mergennachin

docs/source/kernel-library-overview.md

@@ -2,7 +2,7 @@ This page provides a description of the Portable Kernel Library and the Optimize
 
 # Overview of ExecuTorch’s Kernel Libraries
 
-An ExecuTorch program encodes instructions that describe the computation that should be performed by the program. Many of these instructions will correspond to calling a specific ATen operator, for example `aten.convolution`. However, one of the core design principles of ExecuTorch is that the signature of an operator should be separate from the implementation of the operator. This means that the ExecuTorch runtime does not ship with any standard implementation for ATen operators; users must make sure to link against kernel libraries that contain implementations of the operators required by their ExecuTorch program, and configure [operator registration](https://github.com/pytorch/executorch/blob/main/docs/website/docs/tutorials/aten_ops_and_aten_mode.md) to map an operator signature to the desired implementation. This makes it easy to adjust the implementation of operators such as `aten.convolution` that will be called when executing an ExecuTorch program; it allows users to select the exact operator implementations that will meet the unique performance, memory usage, battery usage, etc. constraints of their use-case.
+An ExecuTorch program encodes instructions that describe the computation that should be performed by the program. Many of these instructions will correspond to calling a specific ATen operator, for example `aten.convolution`. However, one of the core design principles of ExecuTorch is that the signature of an operator should be separate from the implementation of the operator. This means that the ExecuTorch runtime does not ship with any standard implementation for ATen operators; users must make sure to link against kernel libraries that contain implementations of the operators required by their ExecuTorch program, and configure [operator registration](./kernel-library-custom-aten-kernel.md) to map an operator signature to the desired implementation. This makes it easy to adjust the implementation of operators such as `aten.convolution` that will be called when executing an ExecuTorch program; it allows users to select the exact operator implementations that will meet the unique performance, memory usage, battery usage, etc. constraints of their use-case.
 
 **In essence, a kernel library is simply a collection of ATen operator implementations that follow a common theme or design principle**. Note that due to ExecuTorch’s selective build process (discussed in the following section), operator implementations are linked individually. This means that users can easily mix different kernel libraries in their build without sacrificing build size.