What Is Neuromorphic Computing?

Neuromorphic computing is a process in which computers are designed and engineered to mirror the structure and function of the human brain. The field of neuromorphic computing still has few real-world applications beyond the research conducted by universities, governments and large tech companies like IBM and Intel Labs. Even so, it shows a lot of promise — particularly in areas that rely on the speed and efficiency of artificial intelligence, such as edge computing, autonomous vehicles and cognitive computing.

Today, the scale of the largest AI computations doubles every three to four months, according to Stanford University professor and neuromorphic computing expert Kwabena Boahen. This rapid growth far outpaces Moore’s Law, which predicts a doubling of computing power every two years. Many experts believe that neuromorphic computing could provide a way around these limitations, offering a more advanced approach to data processing. 

“AI is not going to progress to the point it needs to with the current computers we have,” tech consultant Daniel Bron told Built In. “Neuromorphic computing is way more efficient at running AI. Is it necessary? I can’t say that it’s necessary yet. But it’s definitely a lot more efficient.”

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What Is Neuromorphic Computing?

Neuromorphic computing is an approach where computer components are designed to emulate the human brain and nervous system. Using artificial neurons and synapses, neuromorphic computers simulate the way our brains process information, allowing them to solve problems, recognize patterns and make decisions more quickly and efficiently than the computers we commonly use today. 

“It’s brain-inspired hardware and algorithms,” Andreea Danielescu, an associate director at tech research firm Accenture Labs, told Built In.

How Does Neuromorphic Computing Work?

To understand how neuromorphic computing works, it’s important to understand the cognitive processes it seeks to emulate. 

Neuromorphic architectures are most often modeled after the neocortex in the brain, Bron said. That’s where higher cognitive functions like sensory perception, motor commands, spatial reasoning and language are believed to occur. The neocortex’s layered structure and intricate connectivity are critical to its ability to process complex information and enable human thinking.

The neocortex is made up of neurons and synapses that send and carry information from the brain with near-instantaneous speed and incredible efficiency. It’s what tells your foot to immediately move if you accidentally step on a sharp nail, for example.

Neuromorphic computers try to replicate that efficiency. They do so by forming what are called spiking neural networks. These are formed when spiking neurons, which hold data as if they were biological neurons, are connected via artificial synaptic devices that transfer electrical signals between them. 

A spiking neural network is essentially the hardware version of an artificial neural network, which is a series of algorithms run on a regular computer that mimics the logic of how a human brain thinks. 

How Neuromorphic Computing Differs From Traditional Computing

Neuromorphic computing architecture is a departure from the traditional computer architecture we commonly use today, which is called von Neumann architecture. Von Neumann computers process information in binary, meaning everything is either a one or a zero. And they are inherently sequential, with a clear distinction between data processing (on CPUs) and memory storage (RAM). 

Meanwhile, neuromorphic computers can have millions of artificial neurons and synapses processing different information simultaneously. This gives the system a lot more computational options than von Neumann computers. Neuromorphic computers integrate memory and processing more closely, too, speeding up more data-intensive tasks.

Von Neumann computers have been the standard for decades, and are used for applications ranging from word processing to scientific simulations. But they’re energy inefficient, and often run into data transfer bottlenecks that slow performance. As time goes on, von Neumann architectures will make it increasingly difficult to deliver necessary increases in compute power. This has led researchers to pursue alternative architectures like neuromorphic and quantum.

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Neuromorphic Computing vs. Quantum Computing

Neuromorphic computing and quantum computing are two emerging approaches to computation, each with its own distinct set of characteristics, advantages and applications.

Neuromorphic computing:

• Inspired by the structure and functionality of the human brain
• Uses artificial neurons and synapses to accomplish parallel processing and real-time learning
• Well-suited for tasks involving pattern recognition and sensory processing 
• Logistically easier to accomplish than quantum computing
• More energy-efficient than quantum computing

Quantum computing:

• Leverages principles of quantum mechanics to process information
• Relies on qubits (quantum bits) to run and solve multidimensional quantum algorithms
• Good at efficiently solving complex problems like cryptography and molecular simulation
• Requires lower temperatures and uses more power than neuromorphic computers

Although they are quite different from one another, both neuromorphic and quantum computing hold significant promise in their own rights, and are still very much in the early stages of development and application.

Benefits of Neuromorphic Computing

Neuromorphic computing offers a wide range of benefits, positioning it to be a transformative addition to the world of advanced computing. 

Faster Than Traditional Computing

Neuromorphic systems are designed to imitate the electrical properties of real neurons more closely, which could speed up computation and use less energy. And because they operate in an event-driven way, where neurons only process information when relevant events occur, they can generate responses “pretty much instantly,” Alexander Harrowell, a principal analyst at tech consultancy Omdia, told Built In.

Low latency is always beneficial, but it can make a big difference in tech that relies on real-time sensor data processing, like IoT devices.  

Excellent at Pattern Recognition

Because neuromorphic computers process information in such a massively parallel way, they are particularly good at recognizing patterns. By extension, this means they’re also good at detecting anomalies, Accenture Labs’ Danielescu said, which can be useful in anything from cybersecurity to health monitoring. 

Able to Learn Quickly

Neuromorphic computers are also designed to learn in real time and adapt to changing stimuli, just as humans can, by modifying the strength of the connections between neurons in response to experiences. 

“Neural networks are made to constantly adjust,” Bron said. “They’re made to constantly progress and change, which allows it to get better and better.” 

This versatility can be valuable in applications that require continuous learning and quick decision-making, whether that’s teaching a robot to function on an assembly line or having cars navigate a busy city street autonomously.  

Energy Efficient

One of the most prominent advantages of neuromorphic computing is its energy efficiency, which could be especially beneficial in the making of artificial intelligence — a notoriously energy-intensive industry. 

Neuromorphic computers can process and store data together on each individual neuron, as opposed to having separate areas for each the way von Neumann architectures do. This parallel processing allows multiple tasks to be performed simultaneously, which can lead to faster task completion and lower energy consumption. And spiking neural networks only compute in response to spikes, meaning only a small portion of a system’s neurons use power at any given time while the rest remain idle.

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Challenges of Neuromorphic Computing

While neuromorphic computing has the potential to revolutionize applications of artificial intelligence, data analysis and even our understanding of human cognition, its development faces several challenges.

No Benchmarks or Standardization

Because neuromorphic computing is still a relatively new technology, there are no standard benchmarks for this technology, making it difficult to assess its performance and prove its efficacy outside of a research lab. And the lack of standardized architectures and software interfaces for neuromorphic computing can make it difficult to share applications and results. But Danielescu said there is a “big push” among academic and industry leaders to change this.  

Limited Hardware and Software

Designing and manufacturing neuromorphic hardware that can effectively mimic the complexity of the human brain is a major challenge. That’s because all of the established conventions in computing (how data is encoded, for example) have predominantly evolved within the framework of the von Neumann model. The same goes for software. Most neuromorphic computing being done today is conducted using standard programming languages and algorithms developed for von Neumann hardware, which can limit the results. 

“The proper software building tools don’t really exist for these things,” Bron said. “It’s still very hard to build for it.” 

Difficult to Learn

As of now, neuromorphic computers are only available to experts, and can only be found at multi-billion dollar companies and government-funded research labs. And the technology is not easy to use, Danielescu said, even for people with extensive AI and machine learning backgrounds. It requires extensive knowledge in various domains, including neuroscience, computer science and physics.

In fact, Danielescu estimates there are only a few hundred neuromorphic computing experts globally. “Bridging that gap to allow people with a more traditional computational AI background to move into neuromorphic computing, there just aren’t very many resources for them.” 

Reduced Accuracy and Precision

Machine learning algorithms found to be successful for deep learning applications don’t map directly with spiking neural networks, so they must be adapted. That involves training a deep neural network, converting it into a spiking neural network and mapping it to neuromorphic hardware. This adaptation — and the overall complexity of neuromorphic systems — can cause a reduction in accuracy and precision.

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Neuromorphic Computing Uses

Despite these challenges, neuromorphic computing is still a highly funded field and is projected to exceed $20 billion by 2030. And experts are enthusiastic about its potential to revolutionize various tech fields, thanks to its unique ability to mimic the brain’s information processing and learning capabilities.  

Self-Driving Cars

Self-driving cars must make instant decisions to properly navigate and avoid collisions, which can require extensive computing power. By employing neuromorphic hardware and software, self-driving cars could be able to carry out tasks faster than if they used traditional computing, all with lower energy consumption. This can make for quicker response times and corrections on the road while also keeping overall energy emissions down. 

Drones

Using neuromorphic computing, drones could be just as responsive and reactive to aerial stimuli as living creatures. This technology may allow vision-based drones to autonomously traverse complex terrain or evade obstacles. A neuromorphic-engineered drone can also be programmed to only increase its energy usage when processing environmental changes, allowing it to rapidly respond to sudden crises during rescue or military operations. 

Edge AI

Neuromorphic computing’s energy efficiency, adaptability and ability to process data in real time make it well-suited for edge AI, where computations are done locally on a machine (like a smart device or autonomous vehicle) rather than in a centralized cloud computing facility or offsite data center, requiring the real-time processing of data from things like sensors and cameras. 

With its event-driven and parallel-processing capabilities, neuromorphic computing can enable quick, low-latency decision-making. And its energy efficiency can extend the battery life of these devices, reducing the need to recharge or replace edge devices around the home. In fact, Bron said some studies have found neuromorphic computing to be 100 times more effective in terms of battery efficiency than normal computing. 

Robotics

Neuromorphic systems can enhance the sensory perception and decision-making capabilities of robots, enabling them to better navigate complex environments (like a factory floor), recognize objects and interact with humans more naturally. 

Fraud Detection

Neuromorphic computing excels at recognizing complex patterns, enabling it to identify subtle signs of fraudulent activity or security breaches, such as unusual spending behavior or counterfeit login attempts. Plus, the low latency processing of neuromorphic computing could enable a swifter response once the fraud has been detected, such as freezing accounts or alerting the proper authorities in real time. 

Neuroscience Research

Through its use of brain-inspired neural networks, neuromorphic computing hardware is used to advance our understanding of human cognition. As researchers try to recreate our thought processes in electronics, they may learn more about the brain’s inner workings. 

The Human Brain Project, an EU-funded group made up of some 140 universities, teaching hospitals and research centers, spent ten years attempting to create a human brain using two neuromorphic supercomputers. It concluded its work in September of 2023.

Researchers have also created a national hub for neuromorphic computing in the United States. By providing wider access to neuromorphic computing technology, researchers hope to spearhead more research initiatives in neuroscience, AI and STEM disciplines. 

Neuromorphic Devices

While neuromorphic computing is still in the early stages, a few neuromorphic devices have been invented. Here are a few examples: 

• IBM’s NorthPole: IBM’s NorthPole chip is energy-efficient while being 4,000 times faster than its predecessor TrueNorth — IBM’s first neuromorphic chip with 1 million neurons and 256 million synapses.  
• Intel’s Loihi 2: Loihi 2 is Intel’s second-generation neuromorphic chip that displays greater energy efficiency and 15 times more resource density than the first-generation chip, supporting a broader range of neuro-based algorithms.
• SpiNNaker: Developed at the University of Manchester, a SpiNNaker machine is a parallel computing platform that can simulate one billion simple neurons, making it a key tool for neuroscience research.   
• NeuRRAM: Created by a team of researchers based in the U.S. and China, NeuRRAM is an AI inference chip that is designed to operate with just a “fraction of energy” used by traditional AI chips, supporting AI in edge devices.  

In addition, a team of researchers developed a neuromorphic device called a spin-memristor, which could reduce AI’s energy consumption to one-hundredth of what it currently uses. Scientists at Los Alamos National Library followed this up with the creation of memristors, which can remember previous electrical signals and power the artificial synapses that are the foundation of neuromorphic computers. And researchers in Germany are building neuromorphic computers with the help of microLED technology.  

Neuromorphic computing remains limited in scope, but these advancements promise to make the technology more widely available in the not-so-distant future.

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