https://github.com/OAID/MXNet-HRT

Heterogeneous Run Time version of MXNet. Added heterogeneous capabilities to the MXNet, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original MXNet architecture which users deploy their applications seamlessly.
https://github.com/OAID/MXNet-HRT

arm arm-compute-library arm-gpu arm-neon artificial-intelligence cnn dnn machine-learning mxnet opencl

Last synced: 3 months ago
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Heterogeneous Run Time version of MXNet. Added heterogeneous capabilities to the MXNet, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original MXNet architecture which users deploy their applications seamlessly.

• Host: GitHub
• URL: https://github.com/OAID/MXNet-HRT
• Owner: OAID
• License: apache-2.0
• Created: 2017-07-06T03:30:23.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-02-11T02:54:44.000Z (over 6 years ago)
• Last Synced: 2024-06-12T08:13:15.832Z (5 months ago)
• Topics: arm, arm-compute-library, arm-gpu, arm-neon, artificial-intelligence, cnn, dnn, machine-learning, mxnet, opencl
• Language: C++
• Homepage:
• Size: 26.9 MB
• Stars: 72
• Watchers: 12
• Forks: 30
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-MXNet - MXNet-HRT

README

        
# MXNet-HRT

[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)

MXNet-HRT is a project that is maintained by **OPEN** AI LAB, it uses Arm Compute Library (NEON+GPU) to speed up [MXNet](https://mxnet.incubator.apache.org/) and provide utilities to debug, profile and tune application performance. 

The release version is 0.3.1, 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/MXNet-HRT](https://github.com/OAID/MXNet-HRT)

* 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).

* MXNet is a Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more. See also [MXNet](https://github.com/apache/incubator-mxnet).

### Documents

* [Installation instructions](https://github.com/OAID/MXNet-HRT/blob/master/acl_openailab/installation.md)

* [User Manuals PDF](https://github.com/OAID/MXNet-HRT/blob/master/acl_openailab/user_manual.pdf)

* [Performance Report PDF](https://github.com/OAID/MXNet-HRT/blob/master/acl_openailab/performance_report.pdf)

### Arm Compute Library Compatibility Issues :

There are some compatibility issues between ACL and MXNet Layers, we bypass it to MXNet'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 MXNet based version is [26b1cb9ad0bcde9206863a6f847455ff3ec3c266](https://github.com/apache/incubator-mxnet/tree/26b1cb9ad0bcde9206863a6f847455ff3ec3c266).

## Version 0.3.1 - Feb 09, 2018

Support Arm Compute Library version 17.12

## Version 0.3.0 - Jan 31, 2018

Support Arm Compute Library version 17.12

## Version 0.2.0 - Aug 27, 2017

Support Arm Compute Library version 17.06 with 4 new layers added

* Batch Normalization Layer

* Direct convolution Layer

* Concatenate layer

## Version 0.1.0 - Jul 6, 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

# Issue Report

Encounter any issue, please report on [issue report](https://github.com/OAID/MXNet-HRT/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