Neuromorphic Computing

What is Neuromorphic Computing?

Neuromorphic Computing - a research area at Fraunhofer IPMS

Increasing digitization is constantly driving the demands on electronic hardware. Speed, performance, miniaturization and energy efficiency are becoming increasingly important when it comes to enabling Big Data and Artificial Intelligence (AI) applications.

A promising solution approach is offered by so-called neuromorphic computing, which aims to emulate the self-organizing and self-learning nature of the brain. Fraunhofer IPMS develops materials, technologies and complete hardware solutions with high energy efficiency, especially for edge applications.


Advantages of Neuromorphic Computing

The technological developments are pursued in different stages of expansion. The so-called "deep neural networks" (DNN) have already arrived in the application with the help of classical technologies (e.g. SRAM or flash-based) and initially emulate the parallelism and efficiency of the brain. Further miniaturization and reduction of power consumption for edge applications is possible using new, innovative technologies. The subsequent generation of so-called "Spiking Neural Networks" (SNN) attempts to additionally physically replicate the temporal component of the functionality of neurons and synapses, which enables even higher energy efficiency and plasticity. Again, innovative technology concepts show promise over classical technologies.

For both generations of neuromorphic hardware, Fraunhofer IPMS is exploring crossbar architectures based on non-volatile memories, the ferroelectric field effect transistors. This is done within various European (TEMPO, ANDANTE, STORAIGE) and Fraunhofer internally funded projects. Particularly innovative materials research for future SNNs using Li-based systems is being conducted within the Saxon project MEMION.

Our research projects on Neuromorphic Computing:

Fraunhofer Lighthouse Project


Research project


Vision transformers with ferroelectric oxides

Research project


Innovative compute-in-memory modules for energy-efficient edge AI

Research project


Sensor Edge Cloud for Federated Learning

Research project


Improved energy efficiency of neuromorphic hardware

Research project


New storage technology for edge AI applications

Research project


Innovative storage concepts for neuromorphic computing

Research project


Memristive redox transistors for energy-efficient neuromorphic computing

Research project


T-KOS - Terahertz Technologies