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acl_openailab/README.md

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-<img src="openailab.png" width = "308" height = "88" alt="OPEN AI LAB" align=center />
-
-The **Arm Compute Library** is a collection of low-level software functions optimized for Arm Cortex CPU and Arm Mali GPU architectures, targeted at a variety of use-cases including: **image processing**, **computer vision and machine learning**.  [![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
-
-**OPEN** AI LAB ported the [Caffe](http://caffe.berkeleyvision.org/) to support Arm Compute Library on  Rockchip RK3399. The target OS is Ubuntu 16.04. [![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
-
-# 1. Release Notes
-Please refer to [Release Notes](release_notes.md) for details
-
-# 2. Installation
-Please refer to [Installation](installation.md) for details
-
-# 3. Performance Report
-Please refer to [Performance Report](performance_report.pdf) for details
-
-# 4. User Manual 
-Please refer to [User Manual](user_manual.pdf) for details
+# CaffeOnACL
+[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
+
+CaffeOnACL is a project that is maintained by **OPEN** AI LAB, it uses Arm Compute Library (NEON+GPU) to speed up [Caffe](http://caffe.berkeleyvision.org/) and provide utilities to debug, profile and tune application performance. 
+
+The release version is 0.3.0, is based on [Rockchip RK3399](http://www.rock-chips.com/plus/3399.html) Platform, target OS is Ubuntu 16.04. Can download the source code from [OAID/CaffeOnACL](https://github.com/OAID/CaffeOnACL)
+
+* The ARM Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies. See also [Arm Compute Library](https://github.com/ARM-software/ComputeLibrary).
+* Caffe is a fast open framework for deep learning. See also [Caffe](https://github.com/BVLC/caffe).
+
+### Documents
+* [Installation instructions](https://github.com/OAID/CaffeOnACL/blob/master/acl_openailab/installation.md)
+* [User Manuals PDF](https://github.com/OAID/CaffeOnACL/blob/master/acl_openailab/user_manual.pdf)
+* [Performance Report PDF](https://github.com/OAID/CaffeOnACL/blob/master/acl_openailab/performance_report.pdf)
+
+### Arm Compute Library Compatibility Issues :
+There are some compatibility issues between ACL and Caffe Layers, we bypass it to Caffe's original layer class as the workaround solution for the below issues
+
+* Normalization in-channel issue
+* Tanh issue
+* Softmax supporting multi-dimension issue
+* Group issue
+
+Performance need be fine turned in the future
+
+# Release History
+The Caffe based version is [793bd96351749cb8df16f1581baf3e7d8036ac37](https://github.com/BVLC/caffe/tree/793bd96351749cb8df16f1581baf3e7d8036ac37).
+
+### Version 0.3.0 - Aug 26, 2017
+Support Arm Compute Library version 17.06 with 4 new layers added
+
+* Batch Normalization Layer
+* Direct convolution Layer
+* Locally Connect Layer
+* Concatenate layer
+
+
+### Version 0.2.0 - Jul 2, 2017
+
+Fix the issues:
+
+* Compatible with Arm Compute Library version 17.06
+* When OpenCL initialization fails, even if Caffe uses CPU-mode,it doesn't work properly.
+
+### Version 0.1.0 - Jun 2, 2017 
+
+  Initial version supports 10 Layers accelerated by Arm Compute Library version 17.05 : 
+
+* Convolution Layer
+* Pooling Layer
+* LRN Layer
+* ReLU Layer
+* Sigmoid Layer
+* Softmax Layer
+* TanH Layer
+* AbsVal Layer
+* BNLL Layer
+* InnerProduct Layer
+
+# 3 Issue Report
+Encounter any issue, please report on [issue report](https://github.com/OAID/CaffeOnACL/issues). Issue report should contain the following information :
+
+*  The exact description of the steps that are needed to reproduce the issue 
+* The exact description of what happens and what you think is wrong