AI Development – Covering All Steps of the Process

AI Model Design • Application Development • System Implementation

Solectrix can handle or assist you with all steps of the AI development process. From setting up a prototype AI for smart data acquisition, guiding the machine learning process, to the eventual compilation of the optimized AI model to an FPGA, the creation of a supplementing embedded application or even the entire embedded system – we are here to help you every step of the way!

AI-Powered Applications

AI technology is already shaping various markets that we at Solectrix operate in. One example are Advanced Driver Assistance Systems.

In such applications, machine learning methods based on artificial neural networks, known as “Deep Learning”, can be used to train an AI that reliably detects pedestrians, bicycles, cars or road signs in complex traffic situations. Such an AI can be implemented as an edge device in passenger cars or as part of an intersection assistant system in trucks and buses.

Image Processing and Analysis

The World Seen Through the Eyes of an AI

A digital eye captures a traffic scene and sends the data to an intelligent control unit – the “brain”. The brain processes and interprets the data, then generates a virtual image of the surroundings with classified objects. The resulting knowledge of the surroundings can be used to alert the driver to critical situations or to serve as an information source for the routing decisions of a self-driving car.

Machine learning

How it works: Teaching the AI what to look for in an image

This level of image recognition is achieved through complex neural networks. The machine learning process used to develop such an AI involves databases with millions of tagged images, making the AI model figure out what differentiates a car from a bus, or a child from a dog. Over time, the AI model learns which image characteristics are linked to a dedicated object type, ultimately arriving at the best possible set of transfer functions for the target application’s convolutional neural network.

Technology

Implementing AI in an Embedded Project

Our Preferred FPGA Platforms
Our AI-powered systems are based on a powerful System-on-a-Chip (SoC) by AMD, particularly the Zynq™ 7000 SoC and the Zynq™ UltraScale+™ MPSoC. These SoCs combine ARM® CPUs with programmable logic, making them system cores with an integrated FPGA.

Functions Implemented in FPGA
The FPGA component of the SoC handles the following tasks:

Image Signal Processing (ISP)
This covers the processing of data from image sensors into high-quality WDR (HDR) material. Sensor fusion with other sensor technologies is also possible.
Safety
Guaranteeing reliability through self-supervision routines.
I/O
Handling the communication with system components, standard interfaces, high-speed memory interfaces etc.

To accelerate the inference of neural networks, we offer two options:

Integration of an IP core into the SoC’s FPGA
This way the neural network can be integrated directly into the FPGA logic to enable efficient execution and processing.
Using an external AI acceleration chip
We also offer the option of connecting an external AI acceleration chip via PCIe®. This way the neural network can be executed on dedicated hardware with high computing power.

This flexible architecture enables our customers to choose the appropriate option to benefit from the advantages of either approach depending on their specific requirements.

Our Range of Services

Solectrix can handle or assist you with all steps of the AI development process.

Project Idea
Defining the application
Setup Prototyping AI for Smart Data Acquisition
Prototype implementation of a camera system for recording of training data material
Image Classification & Data Labeling
Annotation of recorded material (marking of object classes and image sections)
Customized Model Architecture & CNN Training on NVIDIA
Setting up a training session

Pruning, Optimization & Quantization
Optimizing the model to reduce number of calculations
Deploy Model for Inference
The final model after training
Compilation to Target Platform
Using the AMD workflow
Creation of Embedded Vision Application
Developing the detector software
Final Deployment on Low-Power Devices
Ready for the market in an embedded system

AI Ecosystem

The Solectrix AI Ecosystem is an original development platform for convolutional neural networks, an advanced toolset to train a Deep Learning model. Its main fields are:

Model Design
This is where the neural network structure is defined. Usually an iterative development.
Data Sets
This involves image data into the training process. Either public image databases or image data acquired by customers via their own camera hardware, labeled as ground truth annotations and separated into training data set, validation data set, and pruning data set.
Training Session
Where the actual learning of network parameters is executed.

Supervision
The training process is observed, creating statistical information that visualizes the development process of the training and its accuracy.
Model Pruning
Where the trained network is simplified/pruned without a notable loss in accuracy while reducing the number of calculations.
Model Deployment
Where the trained network is simplified/pruned without a notable loss in accuracy while reducing the number of calculations.

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Artifical Intelligence