A resistor that works in a "similarly" way to nerve cells in the body could be used to build neural networks for machine learning.
Many large machine learning models rely on increasing amounts of processing power to achieve their results, but this has vast energy costs and produces large amounts of heat.
One proposed solution is analogue machine learning, which works like a brain by using electronic devices similar to neurons to act as the parts of the model. However, these devices have so far not been fast, small or efficient enough to provide advantages over digital machine learning.
Murat One at the Massachusetts Institute of Technology and his colleagues have created a nanoscale resistor that transmits protons from one terminal to another. This functions a bit like a synapse, a connection between two neurons, where ions flow in one direction to transmit information. But these “artificial synapses” are 1000 times smaller and 10,000 times faster than their biological counterparts.
Just as a human brain learns by remodeling the connections between millions of interconnected neurons, so too could machine learning models run on networks of these resistors.
We are doing somewhat similar things like ion transport, but we are now doing it so fast, whereas biology couldn’t, says One, whose device is a million times faster than previous proton-transporting devices. The new artificial synapse, reported in the Feb. 20 issue of Nature Materials, mimics the way synapses in the brain learn through the signals that cross them. This is a significant energy savings over traditional computing, which involves separately processing information and then storing it into memory. Here, the processing creates the memory.
This synapse may one day be part of a more brain-like computer, which could be especially beneficial for computing that works with visual and auditory signals. Examples of this are seen in voice-controlled interfaces and driverless cars. Past efforts in this field have produced high-performance neural networks supported by artificially intelligent algorithms, these are still distant imitators of the brain that depend on energy consuming traditional computer hardware.
When we learn, electrical signals are sent between neurons in our brain. The most energy is needed the first time a synapse traversed. Every time afterward, the connection requires less energy. This is how synapses efficiently facilitate both learning something new and remembering what we’ve learned. The artificial synapse, unlike most other versions of brain-like computing, also fulfills these two tasks simultaneously, and does so with substantial energy savings.
Deep learning algorithms are very powerful but they rely on processors to calculate and simulate the electrical states and store them somewhere else, which is inefficient in terms of energy and time. “Instead of simulating a neural network, our work is trying to make a neural network.
The artificial synapse is based off a battery design. It consists of two thin, flexible films with three terminals, connected by an electrolyte of salty water. The device works as a transistor, with one of the terminals controlling the flow of electricity between the other two.
You must be logged in to post a comment.