Token-picker: Accelerating Attention In Text Generation With Minimized Memory Transfer Via Probability Estimation

Park Junyoung, Kang Myeonggu, Han Yunki, Kim Yanggon, Shin Jaekang, Kim Lee-sup. Arxiv 2024

[Paper]    
Applications Attention Mechanism Efficiency And Optimization Fine Tuning Language Modeling Model Architecture Pretraining Methods Pruning RAG Reinforcement Learning Training Techniques Transformer

The attention mechanism in text generation is memory-bounded due to its sequential characteristics. Therefore, off-chip memory accesses should be minimized for faster execution. Although previous methods addressed this by pruning unimportant tokens, they fall short in selectively removing tokens with near-zero attention probabilities in each instance. Our method estimates the probability before the softmax function, effectively removing low probability tokens and achieving an 12.1x pruning ratio without fine-tuning. Additionally, we present a hardware design supporting seamless on-demand off-chip access. Our approach shows 2.6x reduced memory accesses, leading to an average 2.3x speedup and a 2.4x energy efficiency.

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