Paano Ginagawang Mas Katulad ng Tao ang AI sa Pamamagitan ng mga Memristor

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.

“Hindi ito dahil hindi sapat ang lakas ng aming mga chip o computer para sa anumang ginagawa nila. Ito ay dahil hindi ito sapat na epektibo. Sobra ang konsumo nila ng enerhiya.”

Propesor 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:

• Adaptibong arkitektura: circuitry that can reconfigure itself based on training data.
• Radikal na kahusayan sa enerhiya: in some cases, using as little as 1/100^th the power of a GPU.
• Mas mababang paglabas ng init: reducing the costly cooling requirements that plague today’s AI data centers.

(Maaari kang magbasa pa tungkol sa hardware na espesyal para sa AI, kabilang ang NPUs, sa aming dedikadong ulat hinggil sa paksa.)

“Ang kakayahang bumuo ng mga microchip na ginagaya ang aktwal na aktibidad ng neuron ay nangangahulugang hindi mo kailangan ng maraming kapangyarihan para sa standby o kapag hindi ginagamit ang makina.

Iyon ay maaaring maging isang napakalaking potensyal na kalamangan sa pagkompyut at ekonomiya.

John LaRocco – siyentistang mananaliksik sa psychiatry sa 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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Ang mga Memristor ay mga elektronikong komponent na ginagaya ang mga synapse na nag-uugnay ng neuron 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.”

Propesor 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.

Pinagmulan: 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.

Pinagmulan: 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.”

Propesor 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.

Pinagmulan: 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.

Kaya’t maaaring maging sustainable ang pagganap ng AI sa hinaharap, na may katulad na antas ng intelihensiya nang hindi nasusunog ang enerhiya na hindi natin kayang panatilihin.”

Propesor 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.

Pamumuhunan sa mga Neuromorphic Chipmaker

Intel

Intel (INTC ) 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.

Ang Intel ay isang higante sa sektor ng semiconductor at nag-evolve sa paglipas ng mga taon mula sa pagiging tagapagtatag ng industriya hanggang sa pagiging lider sa agham at inobasyon, na nawalan ng unang puwesto sa dami ng paggawa sa mga kumpanyang tulad ng TSMC ng Taiwan.

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

Ang Intel ay nangunguna sa neuromorphic computing, kabilang ang pamamagitan ng kanilang 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.

Nilikha rin nito ang Intel Neuromorphic Research Community, na kinabibilangan ng Pennsylvania State University, na kasali sa pananaliksik ng vanadium dioxide, pati na rin ng maraming iba pang potensyal na disenyo ng neuromorphic, at higit sa 75 na iba pang grupo ng pananaliksik.

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?”.

Ang Intel ay napakaaktibo rin sa paggaya ng biyolohikal na pandama sa pamamagitan ng pag-uulit ng paraan kung paano gumagana ang ating utak (isang sangay ng neuromorphic computing), isang paksa na tinalakay pa namin sa aming artikulo “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 ”).

Sa pangkalahatan, ang pananaliksik mula sa Intel Lab ay nasa unahan ng inobasyon sa semiconductor, kabilang ang AI, quantum computing, neuromorphic computing, atbp. (Tinalakay namin ang mga pag-unlad ng Intel sa quantum computing sa aming artikulo “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.

Maaari mo ring basahin pa ang tungkol sa kasalukuyang negosyo at mga programang R&D ng Intel sa aming dedikadong ulat sa pamumuhunan.

Pinakabagong Balita at Pag-unlad ng Stock ng Intel (INTC)

Pag-aaral na Binanggit

^{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}