LRAMM -- Low precision approximates GEMM via RSVD
<|reference_start|>LRAMM -- Low precision approximates GEMM via RSVD: Matrix multiplication computation acceleration has been a research hotspot across various domains. Due to the characteristics of some applications, approximate matrix multiplication can achieve significant performance improvements without losing much precision.   In this paper, we propose LRAMM - a high-performance matrix multiplication approximation algorithm that combines mixed-precision quantized matrix multiplication with RSVD techniques, further enhancing efficiency within the error range of low-precision matrix multiplication by utilizing matrix low-rank decomposition technology.<|reference_end|>
@article{gu2024lramm,
    title={LRAMM -- Low precision approximates GEMM via RSVD},
    author={Hongyaoxing Gu},
    journal={arXiv preprint arXiv:2405.16917},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16917},
    primaryClass={math.NA cs.NA cs.PF}
}