Artificial brain synapse shows excellent energy efficiency
The researchers published their findings in the journal Nature Communications, in a paper titled “Protonic solid-state electrochemical synapse for physical neural networks”.
Neural networks attempt to simulate the way learning takes place in the brain, which is based on the gradual strengthening or weakening of the connections between neurons, known as synapses. The core component of this physical neural network is the resistive switch, whose electronic conductance can be controlled electrically. This control, or modulation, emulates the strengthening and weakening of synapses in the brain. In neural networks using conventional silicon microchip technology, the simulation of these synapses is a very energy-intensive process. Most candidate analog resistive devices so far for such simulated synapses have either been very inefficient, in terms of energy use, or performed inconsistently from one device to another or one cycle to the next.
The new system, the researchers say, overcomes both of these challenges.
“We’re addressing not only the energy challenge, but also the repeatability-related challenge that is pervasive in some of the existing concepts out there,” says Bilge Yildiz, a professor of nuclear science and engineering and of materials science and engineering.
The resistive switch in this work is an electrochemical device, made of tungsten trioxide (WO3) and works in a way similar to the charging and discharging of batteries. Ions, in this case protons, can migrate into or out of the crystalline lattice of the material, explains Yildiz, depending on the polarity and strength of an applied voltage. These changes remain in place until altered by a reverse applied voltage — just as the strengthening or weakening of synapses does.
“The mechanism is similar to the doping of semiconductors,” says Ju Li, who is also a professor of nuclear science and engineering and of materials science and engineering. In that process, the conductivity of silicon can be changed by many orders of magnitude by introducing foreign ions into the silicon lattice.
“Traditionally those ions were implanted at the factory,” he says, but with the new device, the ions are pumped in and out of the lattice in a dynamic, ongoing process. The researchers can control how much of the “dopant” ions go in or out by controlling the voltage, and “we’ve demonstrated a very good repeatability and energy efficiency,” he says.
Lab could provide a faster, more reliable and much more
energy efficient approach to physical neural networks, by
using analog ionic-electronic devices to mimic synapses.
Courtesy of the researchers.
Yildiz adds that this process is “very similar to how the synapses of the biological brain work. There, we’re not working with protons, but with other ions such as calcium, potassium, magnesium, etc., and by moving those ions you actually change the resistance of the synapses, and that is an element of learning.”
The process taking place in the tungsten trioxide in their device is similar to the resistance modulation taking place in biological synapses, she says.
“What we have demonstrated here,” Yildiz says, “even though it’s not an optimized device, gets to the order of energy consumption per unit area per unit change in conductance that’s close to that in the brain.”
The materials used in the demonstration of the new device were chosen for their compatibility with present semiconductor manufacturing systems, according to Li. But they include a polymer material that limits the device’s tolerance for heat, so the team is still searching for other variations of the device’s proton-conducting membrane and better ways of encapsulating its hydrogen source for long-term operations.
MIT – www.mit.edu
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