This research advances Block Floating Point (BFP) techniques to address computational bottlenecks in large language model deployment, particularly for nonlinear operations.

• Extends BFP optimization beyond linear operations to challenging nonlinear functions
• Tackles the quadratic computational complexity in Attention mechanisms
• Enables more efficient hardware implementation through specialized formats
• Significantly reduces memory and computational demands for LLM inference

For engineering teams, this work represents a crucial advancement in hardware-software co-design that could enable more efficient LLM deployment on resource-constrained systems and specialized hardware like FPGAs and ASICs.

Pushing the Limits of BFP on Narrow Precision LLM Inference