Update doc (#464)

* update cmake script

* update readme

* Update README.md

* add citation

* add images

* Update README.md

* update

* Update README.md

* Update CONTRIBUTORS.md

* Update README.md

* Update CITATION.cff

* Update README.md

* Update CITATION.cff

* update doc

* Update CONTRIBUTORS.md

* Update LICENSE

CONTRIBUTORS.md

+# Composable Kernel Developers and Contributors
 
-# Developers
+This is the list of developers and contributors to Composable Kernel library
+
+
+## Developers
 [Chao Liu](https://github.com/asroy), [Jing Zhang](https://github.com/zjing14), 2018-2022
 
 [Letao Qin](https://github.com/ltqin), [Qianfeng Zhang](https://github.com/qianfengz), [Liang Huang](https://github.com/carlushuang), [Shaojie Wang](https://github.com/shaojiewang), 2019-2022
@@ -15,12 +19,13 @@ Xiaoyan Zhou, 2020
 [Jianfeng Yan](https://github.com/j4yan), 2021-2022
 
 
-# Product Manager
+## Product Manager
 [Jun Liu](https://github.com/junliume)
 
-# Contributors
-[Dan Yao](https://github.com/danyao12), [Guangzhao Lu](https://github.com/guangzlu), [Raman Jana](https://github.com/ramjana), [Jehandad Khan](https://github.com/JehandadKhan)
 
-# Acknowledgement
-CK team works closely with Meta [AITemplate](???to.be.added???) team ([Bing Xu](https://github.com/antinucleon), Ying Zhang, etc). Most of the lucrative graph optimization opportunities in ML models were identified by AITemplate team, and we also co-designed many high performance fused kernels for AMD GPUs. Without this collaboration, CK would not reach its current potential.
+## Contributors
+[Dan Yao](https://github.com/danyao12), [Guangzhao Lu](https://github.com/guangzlu), [Raman Jana](https://github.com/ramjana), [Jehandad Khan](https://github.com/JehandadKhan), [Wen-Heng (Jack) Chung](https://github.com/whchung)
+
 
+## Acknowledgement
+CK team works closely with Meta [AITemplate](https://github.com/facebookincubator/AITemplate) team ([Bing Xu](https://github.com/antinucleon), [Hao Lu](https://github.com/hlu1), [Ying Zhang](https://github.com/ipiszy), etc). Most of the lucrative graph optimization opportunities in ML models were identified by AITemplate team, and we also co-designed many high performance fused kernels for AMD GPUs. Without this collaboration, CK would not reach its current potential.

LICENSE

-Copyright (c) 2018-    , Advanced Micro Devices, Inc. (Chao Liu, Jing Zhang)
-Copyright (c) 2019-    , Advanced Micro Devices, Inc. (Letao Qin, Qianfeng Zhang, Liang Huang, Shaojie Wang)
-Copyright (c) 2022-    , Advanced Micro Devices, Inc. (Anthony Chang, Chunyu Lai, Illia Silin, Adam Osewski, Poyen Chen, Jehandad Khan)
-Copyright (c) 2019-2021, Advanced Micro Devices, Inc. (Hanwen Chang)
-Copyright (c) 2019-2020, Advanced Micro Devices, Inc. (Tejash Shah)
-Copyright (c) 2020     , Advanced Micro Devices, Inc. (Xiaoyan Zhou)
-Copyright (c) 2021-2022, Advanced Micro Devices, Inc. (Jianfeng Yan)
-
 SPDX-License-Identifier: MIT
 Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 

README.md

 # Composable Kernel
 
 ## Methodology
-Composable Kernel (CK) library aims to provide a programming model for writing performance critical kernels for Machine Learning workloads across multiple architectures including GPUs, CPUs, etc, through general purpose kernel languages, like HIP C++.
+Composable Kernel (CK) library aims to provide a programming model for writing performance critical kernels for machine learning workloads across multiple architectures including GPUs, CPUs, etc, through general purpose kernel languages, like HIP C++.
 
-CK utilizes two concepts to achieve performance portabilatity and code maintainbility:
+CK utilizes two concepts to achieve performance portability and code maintainability:
 * A tile-based programming model
 * Algorithm complexity reduction for complex ML operators, using innovative technique we call "Tensor Coordinate Transformation".
 
@@ -11,7 +11,7 @@ CK utilizes two concepts to achieve performance portabilatity and code maintainb
 
 ## Code Structure
 Current CK library are structured into 4 layers:
-* "Templated Tile Operators"
+* "Templated Tile Operators" layer
 * "Templated Kernel and Invoker" layer
 * "Instantiated Kernel and Invoker" layer
 * "Client API" layer
@@ -90,7 +90,7 @@ Instructions for using CK as a pre-built kernel library are under [client_exampl
 ### Kernel Timing and Verification
 CK's own kernel timer will warn up kernel once, and then run it multiple times
 to get average kernel time. For some kernels that use atomic add, this will cause
-output buffer to be accumulated multiple times, causing verfication failure.
+output buffer to be accumulated multiple times, causing verification failure.
 To work around it, do not use CK's own timer and do verification at the same time.
 CK's own timer and verification in each example and ckProfiler can be enabled or
 disabled from command line.