Practical overview
When it fits
Consider it when adapting or evaluating a Caffe application for an Arm embedded target and you need runtime acceleration plus profiling and tuning utilities.
Who it is for
- Developers deploying Caffe deep-learning applications on Arm-based embedded systems.
- Teams profiling or tuning Caffe application performance on a documented target platform.
Documented uses
- Run Caffe workloads with Arm CPU and GPU acceleration.
- Debug, profile and tune application performance.
- Assess selected Caffe layer behavior against the Arm Compute Library.
Requirements & limits
The project provides source code through a public repository and advertises a BSD license.
- The documented 0.5.0 target is Rockchip RK3399 with Ubuntu 16.04.
- The README identifies Arm Compute Library 17.12 support in version 0.5.0.
- The README notes compatibility workarounds for normalization, Tanh, multidimensional Softmax and Group layers.
- The project notes that performance may need further tuning.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Locate Caffe-HRT installation and user documentation
Find the project’s installation instructions and manuals for the documented RK3399 and Ubuntu 16.04 release.
Before you start
- Access to the OAID/Caffe-HRT repository.
Steps
Open the OAID/Caffe-HRT repository README.
Use its Documents links to open the installation instructions and User Manuals PDF.
Consult those documents for the installation and use stages they describe.
Expected outcome
The repository provides a path to its installation instructions and user manual for the Caffe-HRT release targeting Rockchip RK3399 and Ubuntu 16.04.
Guide limitations
- The README identifies the documentation but does not include the actual installation or runtime steps.
- The documented release targets RK3399 and Ubuntu 16.04; broader platform compatibility is not established here.
- The README notes compatibility issues involving normalization, Tanh, multidimensional Softmax, and Group layers; those cases use Caffe's original layer classes as a workaround.
- The README says performance still needs fine-tuning.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- OAID/Caffe-HRT · Official homepage title · retained in its source language

