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      "statement": "# Mixed-precision training with FP32 master weights and loss scaling\n\nCombined FP16 storage/computation, FP32 master updates and loss scaling to train tested neural networks with reduced activation bandwidth and memory while recovering higher-precision baselines.\n\n## Scientific scope\n\nScaling must avoid overflow and gradients are unscaled before updates or clipping. FP32 updates/accumulation remain; reduced storage does not mean every operation or model trains entirely in FP16. Reported accuracy/resource outcomes are task- and implementation-specific.\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\nSharan Narang; Gregory Diamos; Erich Elsen; Paulius Micikevicius; Jonah Alben; David Garcia; Boris Ginsburg; Michael Houston; Oleksii Kuchaev; Ganesh Venkatesh; Hao Wu\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- [Mixed Precision Training](https://arxiv.org/abs/1710.03740v1)\n",
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