AWhat changed
Developed Quantlib-based BFLOAT16 emulation and mixed-precision dataflow, testing broad training workloads with round-to-nearest-even conversion, FP32 accumulation and master updates.
Scientific scope
Developed Quantlib-based BFLOAT16 emulation and mixed-precision dataflow, testing broad training workloads with round-to-nearest-even conversion, FP32 accumulation and master updates. BFLOAT16 itself and earlier hardware use are inherited, not invented by this study. Wide exponent range avoids FP16-style loss scaling in tested runs, not a theorem of universal convergence or preserved numerical precision. Bias/master weights and accumulation remain FP32; emulated-format accuracy does not independently measure native-hardware speed or energy.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. BFLOAT16 itself and earlier hardware use are inherited, not invented by this study. Wide exponent range avoids FP16-style loss scaling in tested runs, not a theorem of universal convergence or preserved numerical precision. Bias/master weights and accumulation remain FP32; emulated-format accuracy does not independently measure native-hardware speed or energy.
Contributors
- Source-listed authors and technical contributors · SCIENCE: 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.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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