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Elastic Threshold Attention: Learned Contextual Sparsity for Long-Context Decoding
Author1, Author2, Author3, Author4, Author5
sparse attentionlong-context decodinghardware accelerationmodel efficiency
2609.20888
Builder Relevance
1h ago80%
Abstract
Elastic Threshold Attention (ETA) introduces a trainable architecture that enhances long-context decoding efficiency without sacrificing model quality.
Reality Card
Core Claim
ETA achieves hardware-accelerated decoding speed with 85% training sparsity and 38% active decode density, rivaling dense attention methods.
Method / Result
Delivers up to 2.5x wall-clock decode speedups over FlashAttention-2 on sequences up to 512K tokens.
Limitations
The offline calibration algorithm may introduce overhead in domain-specific deployments.
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