embedded world 2023

Energy-autonomous IoT sensors

In Energy Harvesting, we develop technologies and systems for using energy from the environment to power small electronic consumers. The latest development is a so-called NarrowBand IoT module that collects and transmits utility data in a 5G network. To ensure that the modules and sensors can be operated energy independently, they were specially measured and optimized for energy consumption. This opens up new possibilities for autonomously powering not only LPWANs but also other radio systems with higher energy consumption and more advanced functionalities, such as bidirectional communication.

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Maintenance-free mioty®-LPWAN with Energy Harvesting
Energy Harvesting
energy-autonomous IoT sensors (fraunhofer.de)

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embeddif.[ai]

- embeddif.[ai] Joint Lab: We accompany you to the final and the most suited AI pipeline on your hardware or jointly develop a proof of concept.
- embeddif.[ai] Software Suite: You need a kick-start for your new data science team? Then we can provide you with an software framework that can be extended and customized to your requirement. This framework includes the development workflow of AI piplines that can be directly integrated into your firmware by exporting them into C code. In joint workshops we transfer the know-how to your team in order to integrate AI functionalities into embedded systems.

Spiking Neural Networks

With Spiking Neural Networks (SNNs), Fraunhofer IIS is researching a novel neuromorphic hardware design for ultra-low-power applications. Inspired by the human brain, artificial neurons transmit and process relevant data in the form of pulses. In order to experience the structure, processes and functionality of such SNNs live, we present a 3D demo platform visualizing a pulse-based neural network. Visitors can thus immerse themselves in the world of SNNs and discover which mechanisms are hidden behind this architecture.

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More information Neuromorphic Hardware

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Neuromorphic Hardware

Machine Learning applications in embedded devices are a strongly emerging trend. A large number of AI chips has been announced, the first products for embedded AI are on the market. Current neural network architectures like deep neural networks require high computational complexity and power consumption. Neuromorphic hardware in contrast relies on massive parallel processing and performs calculations, e.g. for Machine Learning, faster and with less power. Efficient architectures for neuromorphic hardware with respect to computational performance, power consumption and chip area are therefore a key element for a widespread deployment of neural networks in embedded devices.

IC-Design

As one of the leading IC design facilities in Europe, we develop customized solutions to meet the constantly evolving requirements of industrial applications. Our work being independent of technology and manufacturer, we are able to provide our customers with optimum solutions. Our focus is on development of mixed-signal ASICs, intelligent integrated sensor systems as well as on design solutions for increasingly complex electronic systems. Whether for industrial, communications, automotive or any other applications, we are your partner from the idea to the series production.

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Rficient Ultra-low-power Wake-up-receiver

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Rficient Ultra-low-power Wake-up-receiver

The energy-efficient RFicient® radio receiver technology with a current consumption less than 3 µA can receive radio telegrams in three license-free frequency bands simultaneously and independently of each other. This makes positioning systems for the localization of objects in warehouses possible, but also worldwide mobile applications, such as container tracking without manual frequency switching. RFicient® is also suitable for building automation, smart lighting, electronic shelf labels, remote maintenance and control, and wireless sensor networks in general. Years of maintenance-free operation is possible with very small batteries and solar cells or with energy harvesting. The particularly short response time in milliseconds also enables real-time applications.

Deep SiL testing of embedded HW/SW systems

Deep SiL testing of embedded systems enables an agile development process as used by the SW industry for years. By using virtual hardware models, deep software testing can start way before hardware is available. This allows to deep test your system from the application layer down to the hardware withouth modifying your software which will increase the coverage significantly and produce more robust systems.

Deep SiL testing of embedded HW/SW systems

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Modern System Packaging

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Modern System Packaging Solutions

New Innovative concepts are fundamental in order to meet the challenges for electronic systems owing to extended functionality and increasing miniaturization. Recent packaging-solutions not only have minimal space requirements but also allow a high data throughput with low energy-consumption. Fraunhofer IIS/EAS supports to master the complex design of packaging-solutions, exploiting degrees of freedom while considering the thermal, mechanical and electrical coupling.

Functional Safety

Damage detection is about detecting damage before it causes the component to fail. In our case, the method of thermal spectroscopy is used. By measuring the transient thermal resistance, damage in the package of an integrated circuit or discrete electronic components can be detected before a functional failure occurs. It is then possible to react, i.e. repair or replace the component and thus prevent its failure.

Functional Safety

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embedded world 2023

Messe Nürnberg / March 14, 2023 - March 16, 2023