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FP8 E4M3/E5M2 interchange formats for deep learning

Specified complementary FP8 encodings and tensor-scale conventions, testing clipped FP8 inputs to matrix/convolution operations across training and inference models.

What is recorded here?

Specified complementary FP8 encodings and tensor-scale conventions, testing clipped FP8 inputs to matrix/convolution operations across training and inference models.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

E4M3 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.

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People and systems

Paulius 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

Source-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.

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English catalogue narrative

FP8 E4M3/E5M2 interchange formats for deep learning

Specified complementary FP8 encodings and tensor-scale conventions, testing clipped FP8 inputs to matrix/convolution operations across training and inference models.

Scientific scope

E4M3 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.

Reading status

This 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.

People and systems

Paulius 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

Source-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.

Original sources