<ul><li>Researchers at Princeton University published a technical paper titled 'Hardware-Efficient Attention for Fast Decoding.'</li><li>The paper introduces Grouped-Tied Attention (GTA) and Grouped Latent Attention (GLA) to optimize attention mechanisms for efficient decoding.</li><li>Experiments show that GTA and GLA achieve quality comparable to existing methods while reducing memory usage and enhancing parallel scalability.</li><li>The GLA kernel outperforms FlashMLA in speculative decoding scenarios, leading to reduced latency and increased throughput in online serving benchmarks.</li></ul>

Arithmetic Intensity In Decoding: A Hardware-Efficient Perspective (Princeton University)

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