Welcome to Quantum Magazine's podcast.

Each episode, we bring you stories about developments in science and mathematics.

I'm Susan Vallett.

A new energy-efficient chip fixes an old design flaw to run large-scale AI algorithms on smaller devices, and it reaches

the same accuracy as wasteful digital computers.

That's next.

Quantum Magazine is an editorially independent online publication supported by the Simon's Foundation

to enhance public understanding of science.

Artificial intelligence algorithms can't keep growing at their current pace.

Algorithms like deep neural networks are loosely inspired by the brain,

with multiple layers of artificial neurons linked to each other via numerical values called weights.

But unlike brains, they get bigger every year. And these days, hardware improvements are no longer keeping pace with the enormous amount

of memory and processing capacity required to run these massive algorithms.

Soon, the size of AI algorithms may hit a wall.

And even if we could keep scaling up hardware to meet the demands of AI, there's

another problem. Running them on traditional computers wastes an enormous amount of energy. The high

carbon emissions generated from running large AI algorithms is already harmful for the environment,

and it will only get worse as the algorithms

grow ever more gigantic. One solution called neuromorphic computing takes inspiration from

biological brains to create energy-efficient designs. These chips can outpace digital computers in conserving energy,

but they've had one major flaw.

They've lacked the computational power needed to run a sizable deep neural network,

...