Neuromorphic computing is likely to have high-impact, long-term implications for generative artificial intelligence (AI), a recent GlobalData report indicates.

Although a specialised term, ‘neuromorphic’ simply refers in this instance to computing design that is based on the architecture of the brain.

Neuromorphic computing explained

The concept is expanded upon in GlobalData’s What are the game-changing innovations in AI after generative AI report, which says: “Neuromorphic engineering is based on our understanding of how the brain and its components, such as neurons and synapses, function. It leverages this biological blueprint to guide the creation of computer systems.”

By imitating the functions of neurons in the human brain, the technology allows for parallel processing. These ‘neurons’ can process data in real-time, learning and adapting autonomously to make rapid and ‘intelligent’ decisions in response to external stimuli.

Speaking on the Beyond GenAI: Game-changing AI innovations edition of GlobalData’s Instant Insights podcast, company Analyst Adarsh Jain explained: “To put it simply it’s brain-inspired intelligence; more formally neuromorphic computing is a type of computing approach inspired by the structure and functions of the human brain, utilising principles of parallel processing and machine learning to perform cognitive computing tasks.”

He continued: “We think there are limitless possibilities when AI is repurposed or remodelled after the human brain.”

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By GlobalData

Neuromorphic computing uses

The possibilities of neuromorphic computing in generative AI have been somewhat explored by the team of UC San Diego researchers at Purdue University, who have combined quantum substances (superconducting materials based on copper oxide and metal-insulator transition materials based on nickel oxide) to create “loop devices” reflecting the connections between neurons and synapses. By connecting the devices and exchanging information across networks, the team was able to emulate the biologically-based neural networks in the human brain.

It remains to be seen how these advancements will shape technology, but some companies have already made early moves into the neuromorphic sphere (or ‘hemisphere’, if we’re being neuroscientific about it). This includes Intel with Pohoiki Springs, the neuromorphic research system providing the computational capacity of 100 million neurons, the approximate number of neurons in a cockroach brain.

Jain warned that, in his work with analyst Sourabh Nyalkalkar, they “realised that companies that invest early in the emerging technology of the future and are in the leadership position are hard to dislodge,” emphasising the importance of these early developments.

Neuromorphic computing and generative AI

Generative AI has already proved itself to be a disruptive theme across industries, with the launch of the ChatGPT language model in November 2022 putting the question of neuromorphic computing firmly in the spotlight.

Governments globally have scrambled to address the issue of regulation, as neural networks made tangible the prospect of autonomous technology. ChatGPT was briefly banned in Italy and remains inaccessible in many countries, including North Korea, Iran, China, Cuba and Syria.

Several companies continue to restrict the use of ChatGPT amongst security concerns, including Amazon, JP Morgan, Apple and Samsung, which experienced the leak of sensitive code by an engineer who uploaded it to the software.

However, generative AI will persist. In a GlobalData survey across its network of business-to-business websites, 73% of respondents said that they had ‘some understanding’ of generative AI and 52% expected to experience its disruptive impact in the next five years.