AI for SMART Embedded System

Artificial intelligence

for smart embedded systems

Artificial intelligence doesn't need supercomputers and companies are already collecting data from which valuable insights can be gained with the help of an AI model. Our AI competence center offers you many advantages for implementing AI in your embedded system

AI competence center _

Our specialized know-how for your development

From the model
to the embedded
AI application

In order to bring the possibilities of AI solutions closer to our customers and to offer the best possible support from a single source, we have founded a competence center for AI. Our team with many years of experience in the use of artificial intelligence shows you the possibilities of machine learning and finds the right solution for your project. According to your own wishes and competencies, we complement the team agilely with cloud and security experts as well as software and hardware developers.

Edge Computing

self-sufficient! can run without a cloud connection

KI - Modell

The ki-model is exported from the cloud to the Phytec embedded systems where it is analyzed in the cloud - independent incoming data information from the analysis can be called up at any time

Acquisition of the application-specific sensor data and transmission to a cloud using Phytec hardware

Data Mining

ki solution

Deep Learning

supported cloud service structures and analyzes the data. PHYTEC provides orientation in the jungle of cloud offers and supports the use of a private / local cloud with its own analysis tools

KI analysis cloud

Neutral tool selection

What is the difference between EDGE and CLOUD computing?

You have questions about the use of AI
or do you need support for your project?

The PHYTEC AI Competence Center will be happy to help you.

AI, Machine & Deep Learning _

Has been around for a long time. Why now? What has changed?

The basic ideas and algorithms of machine learning have long been known. In the last 50 years, however, decisive framework conditions have changed that are now leading to the rapid success of AI:

Increased computing power
Liberalization of computing power through cloud computing
Adaptation of computing power
Exponential data increase
Inexpensive storage space
Easy to use open source analysis tools

What is the difference between machine learning, deep learning & big data?

Nothing works without hardware _

Smart ones Embedded systems with integrated
machine learning

Collecting, storing, structuring and analyzing data are the challenges for the use of artificial intelligence. The computationally intensive part of AI is creating a model. At the same time, hardware is required that records this data, preprocesses it, sends it to the computer / server or processes it itself.

In order to guarantee optimal functionality as an edge device, the hardware must be as powerful as it is energy-efficient. PHYTEC combines the building blocks from years of experience in hardware development and kernel / software development with expertise in data analytics. Get started with AI development directly with our AI kits.