Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, system hallucinations, and serious accuracy gaps. 

One possible solution is emerging in the Netherlands. The country is developing a promising ecosystem for neuromorphic computing, which draws on neuroscience to boost IT efficiencies and performance. Billions of euros are being invested in this new form of computing worldwide. The Netherlands aims to become a leader in the market by bringing together startups, established companies, government organisations, and academics in a neuromorphic computing ecosystem.

A Dutch mission to the UK

In March, a Dutch delegation landed in the UK to host an “Innovation Mission” with local tech and government representatives. Top Sector ICT, a Dutch government–supported organisation, led the mission, which sought to strengthen and discuss the future of neuromorphic computing in Europe and the Netherlands. 

We contacted Top Sector ICT, who connected us with one of their collaborators: Dr Johan H. Mentink, an expert in computational physics at Radboud University in the Netherlands. Dr Mentink spoke about how neuromorphic computing can solve the energy, accuracy, and efficiency challenges of our current computing architectures. 

“Current digital computers use power-hungry processes to handle data,” Dr Mentink said. 

“The result is that some modern data centres use so much energy that they even need their own power plant.” 

Computing today stores data in one place (memory) and processes it in another place (processors). This means that a lot of energy is spent on transporting data, Dr Mentink explained. 

In contrast, neuromorphic computing architectures are different at the hardware and software levels. For example, instead of using processors and memories, neuromorphic systems leverage new hardware components such as memristors. These act as both memory and processors. 

By processing and saving data on the same hardware component, neuromorphic computing removes the energy-intensive and error-prone task of transporting data. Additionally, because data is stored on these components, it can be processed more immediately, resulting in faster decision-making, reduced hallucinations, improved accuracy, and better performance. This concept is being applied to edge computing, Industrial IoT, and robotics to drive faster real-time decision-making. 

“Just like our brains process and store information in the same place, we can make computers that would combine data storage and processing in one place,” Dr Mentink explained.  

Early use cases for neuromorphic computing

Neuromorphic computing is far from just experimental. A great number of new and established technology companies are heavily invested in developing new hardware, edge devices, software, and neuromorphic computing applications.

Big tech brands such as IBM, NVIDIA, and Intel, with its Loihi chips, are all involved in neuromorphic computing, while companies in the Netherlands, aligned with a 2024 national white paper, are taking a leading regional role. 

For example, the Dutch company Innatera — a leader in ultra-low power neuromorphic processors — recently secured €15 million in Series-A funding from Invest-NL Deep Tech Fund, the EIC Fund, MIG Capital, Matterwave Ventures, and Delft Enterprises. 

Innatera is just the tip of the iceberg, as the Netherlands continues to support the new industry through funds, grants, and other incentives.

Immediate use cases for neuromorphic computing include event-based sensing technologies integrated into smart sensors such as cameras or audio. These neuromorphic devices only process change, which can dramatically reduce power and data load, said Sylvester Kaczmarek, the CEO of OrbiSky Systems, a company providing AI integration for space technology.  

Neuromorphic hardware and software have the potential to transfer AI running on the edge, especially for low-power devices such as mobile, wearables, or IoT. 

Pattern recognition, keyword spotting, and simple diagnostics — such as real-time signal processing of complex sensor data streams for biomedical uses, robotics, or industrial monitoring — are some of the leading use cases, Dr Kaczmarek explained. 

When applied to pattern recognition and classification or anomaly detection, neuromorphic computing can make decisions very quickly and efficiently, 

Professor Dr Hans Hilgenkamp, Scientific Director of the MESA+ Institute at the University of Twente, agreed that pattern recognition is one of the fields where neuromorphic computing excels. 

“One may also think about [for example] failure prediction in industrial or automotive applications,” he said.   

The gaps creating neuromorphic opportunities

Despite the recent progress, the road to establishing robust neuromorphic computing ecosystems in the Netherlands is challenging. Globalised tech supply chains and the standardisation of new technologies leave little room for hardware-level innovation. 

For example, optical networks and optical chips have proven to outperform traditional systems in use today, but the tech has not been deployed globally. Deploying new hardware involves strategic coordination between the public and private sectors. The global rollout of 5G technology provides a good example of the challenges. It required telcos and governments around the world to deploy not only new antennas, but also smartphones, laptops, and a lot of hardware that could support the new standard. 

On the software side, meanwhile, 5G systems had a pressing need for global standards to ensure integration, interoperability, and smooth deployment. Additionally, established telcos had to move from pure competition to strategic collaboration— an unfamiliar shift for an industry long built on siloed operations.

Neuromorphic computing ecosystems face similar obstacles. The Netherlands recognises that the entire industry’s success depends on innovation in materials, devices, circuit designs, hardware architecture, algorithms, and applications. 

These challenges and gaps are driving new opportunities for tech companies, startups, vendors, and partners. 

Dr Kaczmarek told us that neuromorphic computing requires full-stack integration. This involves expertise that can connect novel materials and devices through circuit design and architectures to algorithms and applications. “Bringing these layers together is crucial but challenging,” he said. 

On the algorithms and software side of things, developing new paradigms of programming, learning rules (beyond standard deep learning backpropagation), and software tools native to neuromorphic hardware are also priorities. 

“It is crucial to make the hardware usable and efficient — co-designing hardware and algorithms because they are intimately coupled in neuromorphic systems,” said Dr Kaczmarek. 

Other industries which have developed or are considering research on neuromorphic computing include healthcare (brain-computer interfaces and prosthetics), agri-food, and sustainable energy. 

Neuromorphic computing modules or components can also be integrated with conventional CMOS, photonics, AI, and even quantum technologies. 

Long-term opportunities in the Netherlands

We asked Dr Hilgenkamp what expertise or innovations are most needed and offer the greatest opportunities for contribution and growth within this emerging ecosystem.

“The long-term developments involve new materials and a lot of research, which is already taking place on an academic level,” Dr Hilgenkamp said. 

He added that the idea of “materials that can learn” brings up completely new concepts in materials science that are exciting for researchers. 

On the other hand, Dr Mentink pointed to the opportunity to transform our economies, which rely on processing massive amounts of data. 

“Even replacing a small part of that with neuromorphic computing will lead to massive energy savings,” he said. 

“Moreover, with neuromorphic computing, much more processing can be done close to where the data is produced. This is good news for situations in which data contains privacy-sensitive information.” 

Concrete examples, according to Dr Mentink, also include fraud detection for credit card transactions, image analysis by robots and drones, anomaly detection of heartbeats, and processing of telecom data.

“The most promising use cases are those involving huge data flows, strong demands for very fast response times, and small energy budgets,” said Dr Mentink. 

As the use cases for neuromorphic computing increase, Dr Mentink expects the development of software toolchains that enable quick adoption of new neuromorphic platforms to see growth. This new sector would include services to streamline deployment.

“Longer-term sustainable growth requires a concerted interdisciplinary effort across the whole computing stack to enable seamless integration of foundational discoveries to applications in new neuromorphic computing systems,” Dr Mentink said. 

The bottom line

The potential of neuromorphic computing has translated into billions of dollars in investment in the Netherlands and Europe, as well as in Asia and the rest of the world. 

Businesses that can innovate, develop, and integrate hardware and software-level neuromorphic technologies stand to gain the most.  

The potential of neuromorphic computing for greater energy efficiency and performance could ripple across industries. Energy, healthcare, robotics, AI, industrial IoT, and quantum tech all stand to benefit if they integrate the technology. And if the Dutch ecosystem takes off, the Netherlands will be in a position to lead the way.

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