Nyströmformer: Approximating self-attention in linear time and memory via the Nyström method
Linear time, not quadratic. Nyströmformer cuts transformer memory by 90% while maintaining accuracy.
Why it matters
A breakthrough in transformer efficiency that addresses one of AI's biggest computational bottlenecks—quadratic scaling of self-attention. This enables deployment of large models on resource-constrained hardware, directly impacting who can build and deploy AI.
The key facts
10 to knowNyströmformer approximates self-attention in linear time and memory (vs. quadratic in standard transformers)
Uses Nyström method for efficient attention computation
Published via Hugging Face blog (Aug 2, 2022)
Reduces computational overhead for transformer architectures
Enables deployment on lower-compute infrastructure
Nyström method approximates self-attention
Achieves linear time complexity vs. quadratic in standard transformers
Achieves linear memory complexity vs. quadratic in standard transformers
Enables edge AI deployment and cost reduction
Published on Hugging Face (Aug 2, 2022)
Go to the source
Hugging Face Bloghuggingface.co