핵심 개념
NoMAD-Attention proposes an efficient algorithm for LLM inference on CPUs by replacing MAD operations with in-register lookups, achieving significant speedups without sacrificing model quality.
초록
Abstract:
NoMAD-Attention leverages SIMD registers for efficient attention computations on CPUs.
Introduction:
LLMs have potential applications in various fields but are expensive to deploy on CPUs.
Expensive Multiply-add Operations:
Attention computations are compute-bound due to MAD operations, posing a bottleneck.
Opportunities and Challenges from Modern CPUs:
SIMD registers offer fast in-register lookups but face size limitations.
Our Proposal: MAD-Free Attention with In-Register Lookups:
NoMAD-Attention replaces MAD operations with in-register lookups for efficient attention computation.
Methodology:
NoMAD-Attention uses three techniques to enable lookup-based attention.
Experiments:
NoMAD-Attention maintains model quality and achieves significant speedups on CPUs.
Ablation Study:
NoMAD-Attention outperforms PQ-Attention and traditional attention in latency.
Related Works:
Various approaches aim to optimize attention mechanisms and matrix multiplication.
Conclusion:
NoMAD-Attention enhances the efficiency of LLM inference on CPU architectures.
Impact Statement:
The study contributes to democratizing LLMs by enabling their operation on CPU cores.
통계
LLMs exhibit emergent abilities in solving complex tasks without fine-tuning.
NoMAD-Attention achieves up to 2× speedup on 4-bit quantized LLaMA-7B-based model.
인용구
"NoMAD-Attention significantly speeds up LLM inference without sacrificing model quality."
"NoMAD-Attention leverages SIMD registers for efficient attention computations on CPUs."