VQV Signal
Reduced Matrix Multiplication (RMM) is a training-free, input-adaptive method that lowers Transformer inference costs by selecting informative slices in matrix multiplications without changing model weights. This approach reduces the high-dimensional matrix products common in large language model i...
Reduced Matrix Multiplication (RMM) is a training-free, input-adaptive method that lowers Transformer inference costs by selecting informative slices in matrix multiplications without changing model weights. This approach reduces the high-dimensional matrix products common in large language model i...
AI-assisted summary based on the listed source.
Transformer-based language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional matrix multiplications. We propose Reduced Matrix Multiplication (RMM), a training-free, input-adaptive inference method that reduces Transformer matrix products by selecting...
Transformer-based language models require costly repeated matrix multiplications during inference, limiting efficiency. RMM offers a way to reduce these computations adaptively, potentially improving inference speed and resource use without retraining.
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