KANs Upend the AL/ML Scene, and We're Ready

By Quadric

July 8, 2024

What’s the biggest challenge for AI/ML? Power consumption. How are we going to meet it? In late April 2024, a novel AI research paper was published by researchers from MIT and CalTech proposing a fundamentally new approach to machine learning networks – the Kolmogorov Arnold Network – or KAN. In the two months since its publication, the AI research field is ablaze with excitement and speculation that KANs might be a breakthrough that dramatically alters the trajectory of AI models for the better – dramatically smaller model sizes delivering similar accuracy at orders of magnitude lower power consumption – both in training and inference.

However, most every semiconductor built for AI/ML can’t efficiently run KANs. But we Quadric can.

First, some background.

The Power Challenge to Train and Infer

Generative AI models for language and image generation have created a huge buzz. Business publications and conferences speculate on the disruptions to economies and the hoped-for benefits to society. But also, the enormous computational costs to training then run ever larger model has policy makers worried. Various forecasts suggest that LLMs alone may consume greater than 10% of the world’s electricity in just a few short years with no end in sight. No end, that is, until the idea of KANs emerged! Early analysis suggests KANs can be 1/10th to 1/20th the size of conventional MLP-based models while delivering equal results.

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