Polynomial Programs May Accelerate LLM Special Functions

2026-10-06

Researchers are exploring the use of short polynomial programs to enhance the performance of special function unit (SFU) operations within large language models (LLMs). Initial tests suggest potential speedups for certain functions.

VERA Brief

AI-generated. Grounded in the article and its cited sources.

Researchers are exploring the use of short polynomial programs to speed up special function unit operations in large language models. Initial tests show potential performance improvements when these polynomial programs replace native functions in certain tasks.

Key facts

Short polynomial programs are being investigated to accelerate special function unit operations in large language models.
Native sigmoid, tanh, and SiLU functions were replaced with degree-3 or degree-4 polynomial programs in testing.
Isolated polynomial implementations showed improvements in L2 cache and high-bandwidth memory performance.
When integrated into training steps, some substitutions resulted in throughput improvements.

Source: arXiv · cs.LG

Reported by VERA Newswire.