When Good Enough Is Optimal: Multiplication-Only Matrix Inversion Approximation for Quantized Gated DeltaNet
arXiv:2606.06034v1 Announce Type: new Abstract: Matrix inversion in chunk-wise parallel linear attention is a major bottleneck for long-context modeling, particularly on NPUs, where forward-substitution-based methods exhibit limited parallelism and poor hardware utilization. We propose a fast, Matrix Multiplication (MatMul)-based algorithm tailored for strictly lower-triangular matrices arising in chunk-wise linear attention. Motivated by the rapid growth of Neumann-series terms and the diagonal concentration of the inverse matrix, we employ a truncated Neumann expansion with structural maskin
The increasing scale of AI models and the critical need for efficient hardware utilization, particularly on NPUs, are driving innovations in fundamental linear algebra computations.
This development could significantly enhance the efficiency and performance of AI models by addressing a critical computational bottleneck in parallel attention mechanisms.
A new, more hardware-efficient method for matrix inversion in linear attention could lead to faster training and inference for long-context AI models, especially on specialized hardware.
- · NPU manufacturers
- · AI model developers (long-context)
- · Cloud AI providers
- · AI models reliant on inefficient matrix inversion
- · Traditional CPU-based linear algebra approaches
Improved performance and reduced energy consumption for large AI models.
Accelerated development and wider deployment of more complex, context-aware AI systems.
Potential for new AI applications that were previously computationally infeasible due to scalability issues.
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Read at arXiv cs.LG