<ul><li>Efficient inference on GPUs using large language models remains challenging due to memory bandwidth limitations.</li><li>SiftAttention is a new approximate attention method proposed to address memory bandwidth limitations in attention computations.</li><li>SiftAttention replaces the top-$k$ step in attention computation with a computationally efficient element-wise filtering operation based on a threshold value.</li><li>The approach dynamically estimates a threshold value per prompt at each generation step, reducing data movement between High Bandwidth Memory (HBM) and SRAM.</li></ul>

Power Law Guided Dynamic Sifting for Efficient Attention

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