Transformer Approximations from ReLUs

A new paper on arXiv from researchers Jerry Yao-Chieh Hu, Mingcheng Lu, Yi-Chen Lee, and Han Liu introduces a systematic recipe for translating ReLU approximation results to the softmax attention mechanism used in transformers. This recipe covers a range of common approximation targets, including multiplication, reciprocal computation, and min/max primitives. Importantly, it yields target-specific, economic resource bounds, going beyond universal approximation statements to provide more practical constraints.

This work provides new analytical tools for analyzing softmax transformer models, potentially enabling more efficient and targeted optimizations. By bridging ReLU and softmax approximations, the method could simplify the design and analysis of transformer architectures, with implications for improving performance and resource allocation in AI systems.