The Lab Mistake That Might Revolutionize Computing
Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why.
Key Takeaways
- AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs capable of executing up to trillions of operations per second.
But each of those GPUs achieves that by consuming as much as 1,000 watts apiece.
- What's more, the simulated artificial neurons that make up these networks lack even a fraction of the complex computing behavior of the biological neurons that comprise the most energy-efficient computing system that we know, the human brain.
The brain is roughly one million times as energy efficient at many of the comparable tasks we set for AI.
- But this approach requires so many transistors (and a few bulky capacitors) that it greatly limits the size of the system that can be constructed, making it unclear how such brain-inspired hardware could ever scale up and compete with state-of-the-art GPUs.
But all along there was an artificial neuron and a synapse-each a single device-hiding in plain sight.
- MOSFETs have evolved in recent years, but their classic form consists of a piece of silicon that has been doped to contain an excess of either positive ( p -type) or negative ( n -type) charge carriers.
- It doesn't typically get much attention, but it's very important to our story.
AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs capable of executing up to trillions of operations per second. But each of those GPUs achieves that by consuming as much as 1,000 watts apiece. For comparison, if you've got a newer smartphone, it probably uses less than 1 W.
That kilowatt figure puts GPUs on the same level as vacuum cleaners, dishwashers, and stoves, but with the big difference that data-center processors are operating uninterrupted around the clock. Fundamentally, a lot of this inefficiency is because GPUs are trying to simulate the workings of artificial neural networks using software and billions of transistors, which requires using energy to move massive amounts of data. What's more, the simulated artificial neurons that make up these networks lack even a fraction of the complex computing behavior of the biological neurons that comprise the most energy-efficient computing system that we know, the human brain.
The brain is roughly one million times as energy efficient at many of the comparable tasks we set for AI. To try to approach these efficiencies , a radically different way of computing called neuromorphic engineering is seeking to build electronic components and circuits that act more like the brain's neurons and the synapses that connect them. Huge amounts of work have gone into making electronics operate more like biological neurons and synapses .
For more details please read the original article at IEEE Spectrum AI.
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