A new technique could put AI models on a strict energy diet, potentially cutting power consumption by up to 95% without compromising quality.

Researchers at BitEnergy AI, Inc. have developed Linear-Complexity Multiplication (L-Mul), a method that replaces energy-intensive floating-point multiplications with simpler integer additions in AI computations.

For those unfamiliar with the term, floating-point is a mathematical shorthand that allows computers to handle very large and very small numbers efficiently by adjusting the placement of the decimal point. You can think of it like scientific notation, in binary. They are essential for many calculations in AI models, but they require a lot of energy and computing power. The bigger the number, the better the model is—and the more computing power it requires. Fp32 is generally a full precision model, with developers reducing precision to fp16, fp8, and even fp4, so their models can run on local hardware.

AI’s voracious appetite for electricity has become a growing concern. ChatGPT alone gobbles up 564 MWh daily—enough to power Go to Source to See Full Article
Author: Jose Antonio Lanz