Fraunhofer Institute for Integrated Circuits IIS
Fraunhofer Institute for Integrated Circuits IIS
3D render AI artificial intelligence technology CPU central processor unit chipset on the printed circuit board for electronic and technology concept select focus shallow depth of field
© istock

© iStock

3D render AI artificial intelligence technology CPU central processor unit chipset on the printed circuit board for electronic and technology concept select focus shallow depth of field
© istock

© iStock

3D render AI artificial intelligence technology CPU central processor unit chipset on the printed circuit board for electronic and technology concept select focus shallow depth of field
© istock

© iStock

3D render AI artificial intelligence technology CPU central processor unit chipset on the printed circuit board for electronic and technology concept select focus shallow depth of field
© istock

© iStock

3D render AI artificial intelligence technology CPU central processor unit chipset on the printed circuit board for electronic and technology concept select focus shallow depth of field
© istock

© iStock

Efficient AI

Energy efficient AI for the future - on-device inference and training

Whether for automating processes or analyzing large volumes of data: Intelligent and self-learning systems are becoming increasingly important in business processes. Until now, these intelligent systems have always had to be connected to a cloud, as this provides the necessary computing power for AI models. With Edge AI, short for Edge Artificial Intelligence, the next generation of intelligent systems is now entering the home: intelligence is being transferred directly to the end devices.

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Benefits of Edge AI

Energy Efficiency and Resource Conservation

The TinyML needs up to a thousand times less power to run ML applications compared to a standard GPU. The reason for this is, among other things, the local execution of the models instead of sending the data back and forth. For this reason, the TinyML devices can run for years without batteries, depending on the use case.

The size of the batteries required and thus the use of valuable resources is also reduced due to the considerable energy savings.

Realtime

 

Since the model is run locally, in order to perform inference, the raw data does not need to be sent to the cloud first and then sent back when processed. This reduces output latency as well as communication bandwidth requirements, which in turn enables rapid response.

Independence, Privacy and Security

The user is not dependent on a cloud service provider. Since the data does not have to be shared with external parties, this point ultimately also contributes to the protection of privacy.

In addition, there is no dependence on a communication link. The risk of possible interference during transmission between the embedded system and the cloud is therefore eliminated.

Become a Certified Data Scientist Specialized in Edge AI

TinyML enables machine learning on microcontrollers and opens doors to a wide range of application areas. The course is designed for specialists with data analysis experience and hardware developers who want to improve their knowledge of machine learning.

You will learn how to develop ML applications on microcontrollers, recognize challenges at an early stage and develop appropriate strategies. Use our comprehensive know-how for your success.

Our Service Offer

Research and Development

We offer you partial or complete R&D services.

 

  • Embedded AI: development tool for developing embedded AI solutions to reduce costs and improve the quality of your application.
  • Optimized AI model for your hardware: a tailor-made solution adapted to your hardware with optimized performance through AI.
  • Mentoring : We accompany you in your R&D projects from data acquisition to the development of an AI model.

Consulting

We advise and support you with your individual concerns relating to your AI solutions.

 

 

  • Potential analyses: We carry out a quick potential analysis of your personal concerns.
  • Directional decisions: We provide you with groundbreaking support by creating an automated report to help you make initial directional decisions on your personal project.
  • Hardware recommendations: We advise you on suitable hardware and give you recommendations for use in your AI solution.
  • Personal support: Our interdisciplinary team and the network at IIS will support you with your personal project.

Licensing

We may have already developed the right AI model and this can be integrated directly into your use case or licensed by you.

 

We optimize your application through standardized processes and extensive automation of time-intensive work specifically adapted to your use case.

Through training and automatic reduction of complex AI models by removing redundancies, we generate optimal AI models in terms of accuracy and efficiency.

We support you with rapid integration!

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Certified Training

We provide you with a quick introduction to AI solutions. Our range of training courses includes AutoML webinars, skills training and seminars on various AI topics.

 

We would be happy to use your data for a customized seminar!

Contact us!

We realize the efficient processing of your R&D projects, as well as the training of junior staff with this new competence profile.

Contact us
for an individual consultation at

machine-learning-lv@iis.fraunhofer.de

Use Cases

 

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Vision

From agriculture and biodiversity to people counting. AI analysis directly behind the camera sensor enables numerous new applications without sending a lot of raw data to the cloud. Privacy remains protected at all times.

Vision
 

Condition Monitoring

Using machine learning in embedded systems to monitor the status of systems and machines in order to be able to react at an early stage or increase efficiency.

Condition Monitoring

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Retail

We've all been there: long queues at the checkout again.

With privacy-protecting AI on edge devices, seamless shopping is already possible.

This leaves more time for the finer things.

 

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Speech & Audio

Machine learning in embedded sensor modules for cognitive speech and audio analysis. The audio commands are recognized without a cloud connection.

Audio Demo
 

© Fraunhofer IIS

Tools

Machine learning in embedded sensor modules for cognitive hand tools to recognize assembly processes and ensure quality.

KI4Tools