On the Structure of Floating-Point Noise in Batch-Invariant GPU Matrix Multiplication
NeutralArtificial Intelligence
A recent study published on arXiv explores the nature of floating-point noise in GPU matrix multiplication, a crucial operation in deep learning. The research challenges the common belief that numerical errors behave like independent Gaussian noise, shedding light on the complexities of these errors. Understanding this can significantly impact the reliability of deep learning models, making this study important for researchers and practitioners in the field.
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