A team of engineers at AI inference technology company BitEnergy AI reports a method to reduce the energy needs of AI applications by 95%. The group has published a paper describing their new technique on the arXiv preprint server.
As AI applications have gone mainstream, their use has risen dramatically, leading to a notable rise in energy needs and costs. LLMs such as ChatGPT require a lot of computing power, which in turn means a lot of electricity is needed to run them.
As just one example, ChatGPT now requires roughly 564 MWh daily, or enough to power 18,000 American homes. As the science continues to advance and such apps become more popular, critics have suggested that AI applications might be using around 100 TWh annually in just a few years, on par with Bitcoin mining operations.
In this new effort, the team at BitEnergy AI claims that they have found a way to dramatically reduce the amount of computing required to run AI apps that does not result in reduced performance.
The new technique is basic—instead of using complex floating-point multiplication (FPM), the method uses integer addition. Apps use FPM to handle extremely large or small numbers, allowing applications to carry out calculations using them with extreme precision. It is also the most energy-intensive part of AI number crunching.
The researchers call their new method Linear-Complexity Multiplication—it works by approximating FPMs using integer addition. They claim that testing, thus far, has shown that the new approach reduces electricity demand by 95%.
The one drawback it has is that it requires different hardware than that currently in use. But the research team also notes that the new type of hardware has already been designed, built and tested.
How such hardware would be licensed, however, is still unclear—currently, GPU maker Nvidia dominates the AI hardware market. How they respond to this new technology could have a major impact on the pace at which it is adopted—if the company’s claims are verified.
More information:
Hongyin Luo et al, Addition is All You Need for Energy-efficient Language Models, arXiv (2024). DOI: 10.48550/arxiv.2410.00907
arXiv
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Integer addition algorithm could reduce energy needs of AI by 95% (2024, October 12)
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