A staff of engineers at AI inference expertise firm BitEnergy AI reviews a technique to scale back the vitality wants of AI functions by 95%. The group has revealed a paper describing their new approach on the arXiv preprint server.
As AI functions have gone mainstream, their use has risen dramatically, resulting in a notable rise in vitality wants and prices. LLMs similar to ChatGPT require lots of computing energy, which in flip means lots of electrical energy is required to run them.
As only one instance, ChatGPT now requires roughly 564 MWh day by day, or sufficient to energy 18,000 American houses. Because the science continues to advance and such apps develop into extra fashionable, critics have urged that AI functions may be utilizing round 100 TWh yearly in just some years, on par with Bitcoin mining operations.
On this new effort, the staff at BitEnergy AI claims that they’ve discovered a method to dramatically scale back the quantity of computing required to run AI apps that doesn’t end in diminished efficiency.
The brand new approach is fundamental—as a substitute of utilizing complicated floating-point multiplication (FPM), the strategy makes use of integer addition. Apps use FPM to deal with extraordinarily massive or small numbers, permitting functions to hold out calculations utilizing them with excessive precision. Additionally it is essentially the most energy-intensive a part of AI quantity crunching.
The researchers name their new methodology Linear-Complexity Multiplication—it really works by approximating FPMs utilizing integer addition. They declare that testing, so far, has proven that the brand new method reduces electrical energy demand by 95%.
The one downside it has is that it requires completely different {hardware} than that at present in use. However the analysis staff additionally notes that the brand new sort of {hardware} has already been designed, constructed and examined.
How such {hardware} could be licensed, nevertheless, continues to be unclear—at present, GPU maker Nvidia dominates the AI {hardware} market. How they reply to this new expertise may have a serious affect on the tempo at which it’s adopted—if the corporate’s claims are verified.
Extra data:
Hongyin Luo et al, Addition is All You Want for Vitality-efficient Language Fashions, arXiv (2024). DOI: 10.48550/arxiv.2410.00907
arXiv
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