How Memristors Are Making AI More Human-Like

The Rise of Neuromorphic, Brain-Like AI Hardware

As AI becomes the center of the tech industry, a growing problem has emerged: the massive computing and energy demand of AI when performed using CPUs and GPUs.

As a result, researchers are working hard on Neural Processing Units (NPUs), also called neuromorphic chips, a type of AI hardware that mimics the brain’s neurons.

“It’s not that our chips or computers are not powerful enough for whatever they are doing. It’s that they aren’t efficient enough. They use too much energy.”

Professor Joshua Yang – University Of Southern California

The shift toward brain-inspired hardware could reshape how we approach artificial intelligence. Neuromorphic designs offer three major advantages over conventional chips:

• Adaptive architecture: circuitry that can reconfigure itself based on training data.
• Radical energy efficiency: in some cases, using as little as 1/100^th the power of a GPU.
• Lower heat output: reducing the costly cooling requirements that plague today’s AI data centers.

(You can read more about AI-specialized hardware, including NPUs, in our dedicated report on the topic.)

“Being able to develop microchips that mimic actual neural activity means you don’t need a lot of power for standby or when the machine isn’t being used.

That’s something that can be a huge potential computational and economic advantage.”

John LaRocco – research scientist in psychiatry at Ohio State’s College of Medicine.

Researchers are testing several promising methods for creating neuromorphic chips. One approach involves leveraging incipient ferroelectricity—a still poorly understood phenomenon that could allow materials to spontaneously switch their electric polarization under the right conditions. Another focuses on active substrates made from vanadium or titanium, materials that can dynamically change their electrical properties to mimic brain-like signaling.

Perhaps the most widely discussed path is the use of memristors—a revolutionary class of electronic components capable of storing information through resistance changes. These devices can perform AI tasks at as little as 1/800^th the normal power consumption, making them one of the most energy-efficient solutions under development.

How Memristors Mimic Synapses

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Memristors are electronic components that mimic neuron-connecting synapses by remembering which electric state they were toggled to after their power is turned off.

This can greatly reduce the energy and time lost from shuttling data back and forth between processors and memory.

One of the key strengths of memristors is their capacity for efficient and self-adaptive in situ learning, which is critical for applications in robotics and autonomous vehicles.

Moreover, the low power consumption of memristors is particularly beneficial in robotics and autonomous vehicles, where energy efficiency is paramount.

Many paths are being explored on how to create the best memristors, from relatively conventional titanium oxide memristors to using actual human neurons (organoids) or even mushrooms.

The idea of using organic material, including actual neurons, to mimic the activity of neurons makes sense on a theoretical level. However, in practice, interfacing such a “computer” to traditional IT systems can be challenging.

 “Our existing computing systems were never intended to process massive amounts of data or to learn from just a few examples on their own.

One way to boost both energy and learning efficiency is to build artificial systems that operate according to principles observed in the brain.”

Professor Joshua Yang – University Of South California

This lack of efficiency is staggering when compared to the human brain. A young child can learn to recognize handwritten digits after seeing only a few examples of each, whereas a computer typically needs thousands to achieve the same task.

And the human brain performs this feat while consuming around 20W of power, while the latest AI data centers are looking at GW-scale, or almost a hundred million times more power.

A new intermediary option could be to create artificial chips that act like neurons in their basic principle. This is the path taken by researchers at the University of Southern California, University of California, University of Massachusetts, Syracuse University, Air Force Research Laboratory, and NASA Ames Research Center.

They published their results in Nature Electronics¹, under the title “A spiking artificial neuron based on one diffusive memristor, one transistor and one resistor”.

Replicating How Neurons Fire Using Diffusive Memristors

How Do Neurons Work?

The way neurons interact with each other, and ultimately process information, is by using both electrical and chemical signals.

If a signal is strong enough, it generates an electrical impulse called an action potential by allowing positively charged sodium ions to flood into the cell.

Source: Nature Electronics

When this electric signal is received, it causes the release of neurotransmitters.

Until now, electronic memristors and complementary metal–oxide–semiconductor (CMOS) circuits have used electric signals to virtually simulate such functioning, requiring hundreds of transistors to simulate a neuron.

Instead, the researchers developed a device called a “diffusive memristor”, which also uses actual chemical interactions to start computational processes.

What Are Diffusive Memristors and How Do They Work?

While traditional silicon systems rely on electrons to perform computations, diffusive memristors use the motion of atoms instead. They use silver ions embedded in oxide materials to generate electrical pulses that mimic natural brain functions.

Source: Nature Electronics

Of course, this does not replicate exactly how a neuron works, but the principle is very similar.

“Even though it’s not exactly the same ions in our artificial synapses and neurons, the physics governing the ion motion and the dynamics are very similar.”

Professor Joshua Yang – University Of South California

In part, this similarity comes from the fact that silver ions are easy to diffuse in this memristor system, similar to how sodium ions can move in organic cells.

Besides silver, the memristor also uses palladium, silicon, titanium, and hafnium. The research could visualize in real time the diffusion of silver in response to an electric stimulus.
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The Future: Neuromorphic Chips & Energy-Efficient AI

A key advantage of this new type of memristor is that it fits within the footprint of a single transistor, whereas older designs required tens or even hundreds.

The initial test used only a handful of such diffusive memristors, demonstrating that they can be used for building the typical multi-level neural network used by almost all AI systems today.

Source: Nature Electronics

The next step will be to assemble many more of such systems to test how efficient they can be at actually performing AI tasks.

“We are designing the building blocks that eventually led us to reduce the chip size by orders of magnitude, and reduce the energy consumption by orders of magnitude.

So it can be sustainable to perform AI in the future, with a similar level of intelligence without burning energy that we cannot sustain.”

Professor Joshua Yang – University Of South California

Finding out if other ions can be used could also be useful, as silver ions are not commonly used in semiconductor manufacturing, which could limit the speed of adoption of this design by the industry.

Another effect of diffusive memristors is that they could help better understand how biological brains work.

In the long run, they are likely to be especially useful for so-called “edge computing”, where computation is done directly on site, like for example with a robot or self-driving car having to make a decision without connection to an AI data center.

Investing in Neuromorphic Chipmakers

Intel (INTC +0.07%)

Intel is a giant in the semiconductor sector and has evolved over the years from a founder of the industry to a scientific and innovation leader, losing the top spot of manufacturing volume to companies like Taiwan’s TSMC.

Intel is a leader in neuromorphic computing, including through its Loihi 2 chip.

It also created the Intel Neuromorphic Research Community, which includes Pennsylvania State University, involved in vanadium dioxide research, as well as many other potential neuromorphic designs, and 75+ other research groups.

Intel is also very active in mimicking biological sense through replicating the way our brain works (itself a branch of neuromorphic computing), something we discussed further in our article “Biomimetic Olfactory Chips: Are Artificial Intelligence and E-Noses the Next Canary in a Coal Mine?”

Overall, research from Intel Lab is at the forefront of semiconductor innovation, including AI, quantum computing, neuromorphic computing, etc. (We discussed Intel advances in quantum computing in our article “The Current State of Quantum Computing”).

You can also read more about Intel’s current business and R&D programs in our dedicated investment report.

Latest Intel (INTC) Stock News and Developments

Study Referenced

^{1. Zhao, R., Wang, T., Moon, T. et al. A spiking artificial neuron based on one diffusive memristor, one transistor, and one resistor. Nature Electronics (2025). https://doi.org/10.1038/s41928-025-01488-x}