HAIBAL Roadmap

The HAIBAL deep learning toolkit project is an ambitious journey that aims to provide the best artificial intelligence development tools.

2021

June

Launch of HAIBAL’s development

Every journey has a beginning and let’s bet that we will succeed in developing a complete deep learning library that is easy to use, ergonomic and suitable for academics and engineers.

July

Making our first convolution in LabVIEW

We are now able to do our first 2D convolution in LabVIEW

 

December

LabVIEW architecture projectΒ 

All the main layers are now coded in native LabVIEW.

2022

February

Successful signal prediction test

This example, implemented natively in the HAIBAL library, aims to understand how to train signal prediction model. Our idea is to help our novice users to start simply with machine learning and then hit the moon !

March

Successful Importation from keras test

Our first version of HDF5 importator is now functionnal and make possible to load, edit and run any model from Keras Tensorflow. We import a VGG 16 model from Keras with success.

April

Successful MNIST test

This example, implemented natively in the HAIBAL library, aims to understand how to train, predict, save and load a basic model. Our idea is to help our novice users to start simply with machine learning and then hit the moon !

May

Successful Tiny yolo import from keras and run test

Importing a Tiny YoloV3 Model from Keras with success.

 

June

Optimisation V1

In order to better optimize our inference, we have adopted a new approach to memory. The kernel memory system is now managed in C language.

August

Cuda integration v1

Model run now on Nvidia GPU.

 

September

Launch of our youtube channel

To improve our visibility and better communicate with our community, we are launching our youtube channel on which we will share training videos and applications using HAIBAL.

December

Release of HAIBAL 1.0

After one year of development we are proud to release the first version of HAIBAL, the LabVIEW deep learning toolkit. Β 

2023

February

Academia program

Give the student access to the HAIBAL toolkit through our university sponsorship program.

March

Ambassador program

A new premium program of selective partnership for independents allowing them to promote and distribute the HAIBAL toolkit.

Cuda integration improvment

The full parallelism process is now possible with our new cuda memory manager.

New save file system

We adopt H5 and JSON file format to save HAIBAL models.

Keras tensorflow compatibility

We integrate the load of H5 keras model format.

Upcoming

Pytorch compatibility

We integrate the load of pt Pytorch model format.

Upcoming

Reinforcement learning examples integrations

Release of some reinforcement learning examples.

Upcoming

XILINX FPGA ULTRASCALE +

One of our goal is to make HAIBAL running on Xilinx FPGA ultrascale+ offering our custom new possibilities.

Upcoming

Intel oneAPI

We will integrate Intel oneAPI toolkit making HAIBAL compatible with the INTEL Multiarchitecture Open Accelerated Computing (CPU / GPU / FPGA)

Upcoming

Ubuntu

HAIBAL will be available on UBUNTU

Upcoming

Release of our computer vision module

We will integrate our own vision module into the toolkit. It will allow our users to easily capture their data from a camera. This module will also allow to add overlay and segmentation mask.

UpcomingΒ 

NI partnership

HAIBAL deep learning toolkit will also be distributed by National Instuments.

Upcoming

Education program

Implementation of the training and our deep learning certifications.