Modern computer chips handle data at the mind-blowing rate of some 10^13 bits per second. Neurons, by comparison, fire at a rate of around 100 times per second or so. And yet the brain outperforms the best computers in numerous tasks.
One reason for this is way computations take place. In computers, calculations occur in strict pipelines, one at a time.
In the brain, however, many calculations take place at once. Each neuron communicates with up to 1000 other neurons at any one time. And since the brain consists of billions neurons, the potential for parallel calculating is clearly huge.
Computer scientists are well aware of this difference and have tried in many ways to mimic the brain’s massively parallel capabilities. But success has been hard to come by.
Today, Anirban Bandyopadhyay at National Institute for Materials Science in Tsukuba, Japan, unveil a promising new approach. At the heart of their experiment is a ring-like molecule called 2,3-dichloro-5,6-dicyano-p-benzoquinone, or DDQ.
This has an unusual property: it can exist in four different conducting states, depending on the location of trapped electrons around the ring. What’s more, it’s possible to switch the molecule from one to state to another by zapping it with voltages of various different strengths using the tip of a scanning tunnelling microscope. It’s even possible to bias the possible states that can form by placing the molecule in an electric field
Place two DDQ molecules next to each other and it’s possible to make them connect. In fact, a single DDQ molecule can connect with between 2 and 6 neighbours, depending on its conducting state and theirs. When one molecule changes its state, the change in configuration ripples from one molecule to the next, forming and reforming circuits as it travels.
Given all this, it’s not hard to imagine how a layer of DDQ molecules can act like a cellular automaton, with each molecule as a cell in the automaton. Roughly speaking, the rules for flipping cells from one state to another are set by the bias on the molecules and the starting state is programmed by the scanning tunnelling microscope.
And that’s exactly what these guys have done. They’ve laid down 300 DDQ molecules on a gold substrate, setting them up as a cellular automaton. More impressive still, they’ve then initialised the system so that it “calculates” the way heat diffuses in a conducting medium and the way cancer spreads through tissue.
And since the entire layer is involved in the calculation, this a massively parallel computation using a single layer of organic molecules.
Bandyopadhyay and co say the key feature of this type of calculation is the fact that one DDQ molecule can link to many others, rather like neurons in the brain. “Generalization of this principle would…open up a new vista of emergent computing using an assembly of molecules,” they say.
Clearly an intriguing prospect.
Ref: arxiv.org/abs/1110.5844: Massively Parallel Computing An An Organic Molecular Layer
Capitalizing on machine learning with collaborative, structured enterprise tooling teams
Machine learning advances require an evolution of processes, tooling, and operations.
The Download: how to fight pandemics, and a top scientist turned-advisor
Plus: Humane's Ai Pin has been unveiled
The race to destroy PFAS, the forever chemicals
Scientists are showing these damaging compounds can be beat.
How scientists are being squeezed to take sides in the conflict between Israel and Palestine
Tensions over the war are flaring on social media—with real-life ramifications.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.