IBM scientists have created artificial neurons and synapses using phase change memory (PCM) that mimics the brain's cognitive learning capability.
It is the first time the researchers were able to create what they described as "randomly spiking neurons" using phase-change materials to store and process data. The discovery is a milestone in developing energy-sipping and highly dense neuro networks that could be used for cognitive computing applications.
In short, the technology can be used to improve today's processors in order to perform computations in applications such as data-correlation detection for the Internet of Things (IoT), stock market trades and social media posts at a staggeringly fast rate.
The results of IBM's research, "Stochastic Phase-Change Neurons," is 10 years in the making and appeared today on the cover of the peer-reviewed journal Nature Nanotechnology.
IBM Fellow Evangelos Eleftheriou said it will still be "several years" before the market would see a PCM processing chip. But the recent discovery is a critical breakthrough in their development.
PCM's randomness imitates brain neurons
Inspired by the way the human brain functions, scientists have theorized for decades that it should be possible to imitate the versatile computational capabilities of large populations of neurons. However, doing so at the densities and miniscule voltage comparable to biological systems has been a significant challenge -- until now.
Key to the technology is the artificial neurons' random variation or "stochastic" behavior or random behavior.
In statistics, a random variable can be used to determine possible outcomes in data analytics; in other words, it can determine the likelihood of data correlations.
"Basically, it operates how the brain operates, with short voltage pulses coming in through synapses exciting neurons," said Tomas Tuma, lead author of the paper and a scientist at IBM Research in Zurich. "So we use [a] short pulse of, say, nanosecond duration...to induce change in the material."
The PCM's stochasticity, Tuma said, is of key importance in population-based computing where every neuron responds differently and enables new ways to represent signals and compute.
"Normally, people try to hide [stochasticity], or if you want good quality stochasticity you have to induce it artificially. Here, we have shown we have a very nice stochasticity natively because we understand the processes of crystallization and amorphization in phase-change cells," Tuma said.
The phase-change artificial neurons being created today are just 90 nanometers (nm) in size, but the researchers said they have to potential to shrink the process to as small as 14nm in size (a nanometer is one billionth of a meter).
IBM researchers have organized hundreds of artificial neurons into populations and used them to represent fast and complex signals. The artificial neurons have also been shown to sustain billions of switching cycles, which would correspond to multiple years of operation at an update frequency of 100 Hz. The energy required for each neuron update was less than five picojoule and the average power less than 120 microwatts. By comparison, it takes 60 million microwatts to power a 60-watt lightbulb.
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