MXNet-HRT

MXNet-HRT is a project that is maintained by OPEN AI LAB, it uses Arm Compute Library (NEON+GPU) to speed up MXNet and provide utilities to debug, profile and tune application performance.

The release version is 0.3.1, is based on Rockchip RK3399 Platform, target OS is Ubuntu 16.04. Can download the source code from 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.
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.

Documents

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.

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. 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