This research demonstrates how to achieve significant acceleration in transformer models by applying 2:4 sparsity patterns to Squared-ReLU activations without sacrificing model accuracy.

• Up to 1.3x faster Feed Forward Networks in both forward and backward passes
• Exploits intrinsic sparsity already present in Squared-ReLU activations
• Achieves acceleration with zero accuracy loss
• Applies hardware-accelerated sparsity patterns optimized for GPUs

This engineering advancement has significant implications for both training and inference efficiency, potentially reducing computational costs and energy consumption for large language models.

Accelerating Transformer Inference and Training with 2:4 Activation Sparsity