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    "title": "FP8 E4M3/E5M2 interchange formats for deep learning",
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      "statement": "# FP8 E4M3/E5M2 interchange formats for deep learning\n\nSpecified complementary FP8 encodings and tensor-scale conventions, testing clipped FP8 inputs to matrix/convolution operations across training and inference models.\n\n## Scientific scope\n\nE4M3 reassigns special-value patterns and lacks infinities; E5M2 follows IEEE-like special values. Experiments simulate FP8 tensor conversion while arithmetic and many outputs remain wider precision. Per-tensor scaling is required for some models and its optimization is outside the format specification; MobileNet v2 retained an accuracy gap. Not a claim that arbitrary networks use exclusively 8-bit arithmetic.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nPaulius Micikevicius; Dusan Stosic; Patrick Judd; John Kamalu; Stuart Oberman; Mohammad Shoeybi; Michael Siu; Hao Wu; Neil Burgess; Sangwon Ha; Richard Grisenthwaite; Marius Cornea; Alexander Heinecke; Naveen Mellempudi; Pradeep Dubey\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [FP8 Formats for Deep Learning](https://arxiv.org/abs/2209.05433v1)\n",
      "summary": "Specified complementary FP8 encodings and tensor-scale conventions, testing clipped FP8 inputs to matrix/convolution operations across training and inference models.",
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