Tag Archives: Tae Joon Park

An ‘artificial brain’ and life-long learning

Talk of artificial brains (also known as, brainlike computing or neuromorphic computing) usually turns to memory fairly quickly. This February 3, 2022 news item on ScienceDaily does too although the focus is on how memory and forgetting affect the ability to learn,

When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned.

As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper publishing in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time.

“The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan,” said Shriram Ramanathan, a professor in Purdue University’s [Indiana, US] School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.

Unlike the brain, which constantly forms new connections between neurons to enable learning, the circuits on a computer chip don’t change. A circuit that a machine has been using for years isn’t any different than the circuit that was originally built for the machine in a factory.

This is a problem for making AI more portable, such as for autonomous vehicles or robots in space that would have to make decisions on their own in isolated environments. If AI could be embedded directly into hardware rather than just running on software as AI typically does, these machines would be able to operate more efficiently.

A February 3, 2022 Purdue University news release (also on EurekAlert), which originated the news item, provides more technical detail about the work (Note: Links have been removed),

In this study, Ramanathan and his team built a new piece of hardware that can be reprogrammed on demand through electrical pulses. Ramanathan believes that this adaptability would allow the device to take on all of the functions that are necessary to build a brain-inspired computer.

“If we want to build a computer or a machine that is inspired by the brain, then correspondingly, we want to have the ability to continuously program, reprogram and change the chip,” Ramanathan said.

Toward building a brain in chip form

The hardware is a small, rectangular device made of a material called perovskite nickelate,  which is very sensitive to hydrogen. Applying electrical pulses at different voltages allows the device to shuffle a concentration of hydrogen ions in a matter of nanoseconds, creating states that the researchers found could be mapped out to corresponding functions in the brain.

When the device has more hydrogen near its center, for example, it can act as a neuron, a single nerve cell. With less hydrogen at that location, the device serves as a synapse, a connection between neurons, which is what the brain uses to store memory in complex neural circuits.

Through simulations of the experimental data, the Purdue team’s collaborators at Santa Clara University and Portland State University showed that the internal physics of this device creates a dynamic structure for an artificial neural network that is able to more efficiently recognize electrocardiogram patterns and digits compared to static networks. This neural network uses “reservoir computing,” which explains how different parts of a brain communicate and transfer information.

Researchers from The Pennsylvania State University also demonstrated in this study that as new problems are presented, a dynamic network can “pick and choose” which circuits are the best fit for addressing those problems.

Since the team was able to build the device using standard semiconductor-compatible fabrication techniques and operate the device at room temperature, Ramanathan believes that this technique can be readily adopted by the semiconductor industry.

“We demonstrated that this device is very robust,” said Michael Park, a Purdue Ph.D. student in materials engineering. “After programming the device over a million cycles, the reconfiguration of all functions is remarkably reproducible.”

The researchers are working to demonstrate these concepts on large-scale test chips that would be used to build a brain-inspired computer.

Experiments at Purdue were conducted at the FLEX Lab and Birck Nanotechnology Center of Purdue’s Discovery Park. The team’s collaborators at Argonne National Laboratory, the University of Illinois, Brookhaven National Laboratory and the University of Georgia conducted measurements of the device’s properties.

Here’s a link to and a citation for the paper,

Reconfigurable perovskite nickelate electronics for artificial intelligence by Hai-Tian Zhang, Tae Joon Park, A. N. M. Nafiul Islam, Dat S. J. Tran, Sukriti Manna, Qi Wang, Sandip Mondal, Haoming Yu, Suvo Banik, Shaobo Cheng, Hua Zhou, Sampath Gamage, Sayantan Mahapatra, Yimei Zhu, Yohannes Abate, Nan Jiang, Subramanian K. R. S. Sankaranarayanan, Abhronil Sengupta, Christof Teuscher, Shriram Ramanathan. Science • 3 Feb 2022 • Vol 375, Issue 6580 • pp. 533-539 • DOI: 10.1126/science.abj7943

This paper is behind a paywall.

Artificial intelligence (AI) consumes a lot of energy but tree-like memory may help conserve it

A simulation of a quantum material’s properties reveals its ability to learn numbers, a test of artificial intelligence. (Purdue University image/Shakti Wadekar)

A May 7, 2020 Purdue University news release (also on EurekAlert) describes a new approach for energy-efficient hardware in support of artificial intelligence (AI) systems,

To just solve a puzzle or play a game, artificial intelligence can require software running on thousands of computers. That could be the energy that three nuclear plants produce in one hour.

A team of engineers has created hardware that can learn skills using a type of AI that currently runs on software platforms. Sharing intelligence features between hardware and software would offset the energy needed for using AI in more advanced applications such as self-driving cars or discovering drugs.

“Software is taking on most of the challenges in AI. If you could incorporate intelligence into the circuit components in addition to what is happening in software, you could do things that simply cannot be done today,” said Shriram Ramanathan, a professor of materials engineering at Purdue University.

AI hardware development is still in early research stages. Researchers have demonstrated AI in pieces of potential hardware, but haven’t yet addressed AI’s large energy demand.

As AI penetrates more of daily life, a heavy reliance on software with massive energy needs is not sustainable, Ramanathan said. If hardware and software could share intelligence features, an area of silicon might be able to achieve more with a given input of energy.

Ramanathan’s team is the first to demonstrate artificial “tree-like” memory in a piece of potential hardware at room temperature. Researchers in the past have only been able to observe this kind of memory in hardware at temperatures that are too low for electronic devices.

The results of this study are published in the journal Nature Communications.

The hardware that Ramanathan’s team developed is made of a so-called quantum material. These materials are known for having properties that cannot be explained by classical physics. Ramanathan’s lab has been working to better understand these materials and how they might be used to solve problems in electronics.

Software uses tree-like memory to organize information into various “branches,” making that information easier to retrieve when learning new skills or tasks.

The strategy is inspired by how the human brain categorizes information and makes decisions.

“Humans memorize things in a tree structure of categories. We memorize ‘apple’ under the category of ‘fruit’ and ‘elephant’ under the category of ‘animal,’ for example,” said Hai-Tian Zhang, a Lillian Gilbreth postdoctoral fellow in Purdue’s College of Engineering. “Mimicking these features in hardware is potentially interesting for brain-inspired computing.”

The team introduced a proton to a quantum material called neodymium nickel oxide. They discovered that applying an electric pulse to the material moves around the proton. Each new position of the proton creates a different resistance state, which creates an information storage site called a memory state. Multiple electric pulses create a branch made up of memory states.

“We can build up many thousands of memory states in the material by taking advantage of quantum mechanical effects. The material stays the same. We are simply shuffling around protons,” Ramanathan said.

Through simulations of the properties discovered in this material, the team showed that the material is capable of learning the numbers 0 through 9. The ability to learn numbers is a baseline test of artificial intelligence.

The demonstration of these trees at room temperature in a material is a step toward showing that hardware could offload tasks from software.

“This discovery opens up new frontiers for AI that have been largely ignored because implementing this kind of intelligence into electronic hardware didn’t exist,” Ramanathan said.

The material might also help create a way for humans to more naturally communicate with AI.

“Protons also are natural information transporters in human beings. A device enabled by proton transport may be a key component for eventually achieving direct communication with organisms, such as through a brain implant,” Zhang said.

Here’s a link to and a citation for the published study,

Perovskite neural trees by Hai-Tian Zhang, Tae Joon Park, Shriram Ramanathan. Nature Communications volume 11, Article number: 2245 (2020) DOI: https://doi.org/10.1038/s41467-020-16105-y Published: 07 May 2020

This paper is open access.