[DOCS] Introduce how to add hardware backend to FAQ (#4898)

docs/api/python/target.rst

@@ -19,4 +19,4 @@ tvm.target
 ----------
 .. automodule:: tvm.target
     :members:
-   :imported-members:
+    :imported-members:

docs/dev/relay_bring_your_own_codegen.rst

@@ -15,6 +15,8 @@
     specific language governing permissions and limitations
     under the License.
 
+.. _relay-bring-your-own-codegen:
+
 =============================
 Bring Your Own Codegen To TVM
 =============================

docs/faq.md → docs/faq.rst

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+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
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+    with the License.  You may obtain a copy of the License at
+
+..    http://www.apache.org/licenses/LICENSE-2.0
+
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+    software distributed under the License is distributed on an
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+    under the License.
+
 
 Frequently Asked Questions
 ==========================
-This document contains frequently asked questions.
+
 
 How to Install
 --------------
-See [Installation](http://docs.tvm.ai/install/)
+See :ref:`installation`.
+
+
+How to add a new Hardware Backend
+---------------------------------
+
+- If the hardware backend has LLVM support,
+  then we can directly generate the code by setting the correct target triple as in :py:mod:`~tvm.target`.
+- If the target hardware is a GPU, try to use the cuda, opencl or vulkan backend.
+- If the target hardware is a special accelerator,
+  checkout :ref:`vta-index` and :ref:`relay-bring-your-own-codegen`.
+- For all of the above cases, You may want to add target specific
+  optimization templates using AutoTVM, see :ref:`tutorials-autotvm-sec`.
+- Besides using LLVM's vectorization, we can also embed micro-kernels to leverage hardware intrinsics,
+  see :ref:`tutorials-tensorize`.
+
 
 TVM's relation to Other IR/DSL Projects
 ---------------------------------------
@@ -41,9 +57,8 @@ end-to-end compilation stack.
 
 
 TVM's relation to libDNN, cuDNN
-------------------------------
+-------------------------------
 TVM can incorporate these libraries as external calls. One goal of TVM is to be able to
 generate high-performing kernels. We will evolve TVM an incremental manner as
 we learn from the techniques of manual kernel crafting and add these as primitives in DSL.
-See also [TVM Operator Inventory](https://github.com/apache/incubator-tvm/tree/master/topi) for
-recipes of operators in TVM.
+See also top for recipes of operators in TVM.

docs/install/index.rst

@@ -15,6 +15,8 @@
     specific language governing permissions and limitations
     under the License.
 
+.. _installation:
+
 Installation
 ============
 To install TVM, please read :ref:`install-from-source`.

docs/vta/index.rst

@@ -15,6 +15,8 @@
     specific language governing permissions and limitations
     under the License.
 
+.. _vta-index:
+
 VTA: Deep Learning Accelerator Stack
 ====================================
 
@@ -50,4 +52,4 @@ Literature
 - Read the VTA tech report: `An Open Hardware Software Stack for Deep Learning`_.
 
 .. _release blog post: https://tvm.apache.org/2018/07/12/vta-release-announcement
-.. _An Open Hardware Software Stack for Deep Learning: https://arxiv.org/abs/1807.04188
\ No newline at end of file
+.. _An Open Hardware Software Stack for Deep Learning: https://arxiv.org/abs/1807.04188

tutorials/autotvm/README.txt

-Auto tuning
--------------
+.. _tutorials-autotvm-sec:
 
+Auto tuning
+-----------

tutorials/language/tensorize.py

@@ -15,6 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 """
+.. _tutorials-tensorize:
+
 Use Tensorize to Leverage Hardware Intrinsics
 =============================================
 **Author**: `Yizhi Liu <https://github.com/yzhliu>`_