Neuromorphic Computing

What is Neuromorphic Computing?

Neuromorphic Computing - a research area at Fraunhofer IPMS

As digitization continues to accelerate, the demands on electronic hardware are increasing at an unprecedented rate. Speed, performance, miniaturization, and energy efficiency have become essential factors in enabling the next generation of Big Data and Artificial Intelligence (AI) applications, which require rapid processing of vast amounts of data.

A highly promising solution to meet these challenges is neuromorphic computing. This innovative approach seeks to replicate the brain’s ability to self-organize, self-learn, and process information in a highly efficient manner. At Fraunhofer IPMS, we are at the forefront of developing advanced materials, groundbreaking technologies, and fully integrated hardware solutions that are optimized for energy efficiency. Our work is particularly focused on edge applications, where efficient processing is crucial to enable real-time decision-making and data processing at the source.

Advantages of Neuromorphic Computing

Neuromorphic computing is advancing through several stages of development, each bringing new capabilities that further enhance efficiency and performance. The first major breakthrough came with "deep neural networks" (DNN), which have already been successfully applied using traditional technologies such as SRAM or flash-based memory. These classical approaches effectively emulate the parallelism and efficiency seen in the brain, enabling a range of AI applications. However, the next frontier lies in further miniaturization and a significant reduction in power consumption, especially for edge applications, which require real-time, on-site processing with minimal energy use. This progress is made possible by leveraging new, innovative technologies that promise to enhance the performance of DNNs even further.

The subsequent generation of neuromorphic computing, based on "Spiking Neural Networks" (SNN), aims to take this even further by physically replicating the temporal component of neuronal and synaptic functions. By modeling not only the parallelism but also the timing of signals between neurons, SNNs can achieve higher energy efficiency, greater plasticity, and more brain-like learning capabilities. This results in a system that can more efficiently process dynamic, time-varying data while using significantly less energy than traditional approaches. As with DNNs, emerging technology concepts for SNNs are showing great promise in surpassing the limitations of classical technologies.

At Fraunhofer IPMS, we are pioneering research in both generations of neuromorphic hardware. We are exploring innovative crossbar architectures that incorporate non-volatile memory, such as ferroelectric field-effect transistors (FeFETs), which show great potential for neuromorphic applications. These efforts are part of various European-funded initiatives, including TEMPO, ANDANTE, and STORAIGE, as well as Fraunhofer-funded internal projects. Of particular interest is our cutting-edge research into materials for future SNNs, with a focus on Li-based systems, being conducted within the Saxon project MEMION. This innovative materials research is key to unlocking the next level of energy-efficient, brain-inspired computing.

Our research projects on Neuromorphic Computing:

Fraunhofer Lighthouse Project

NeurOSmart

Project

3DFerroKI

Hardware-based AI with 3-dimensional ferroelectric memories 

Research project

FerroSAFE

Field-induced crystallization for robust safety applications

Research project

Prevail

Technology platform for  neuromorphic chips

Research project

Smart IR

 AI-based infrared sensors

Research project

FeEdge

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

Research project

ViTFOX

Vision transformers with ferroelectric oxides

Completed projects

Research project

MEMION

Memristive redox transistors for energy-efficient neuromorphic computing

Research project

StorAIge

New storage technology for edge AI applications

Research project

ANDANTE

Innovative storage concepts for neuromorphic computing

Research project

SEC-Learn

Sensor Edge Cloud for Federated Learning

Research project

TEMPO

Improved energy efficiency of neuromorphic hardware

Research project

T-KOS

T-KOS - Terahertz Technologies