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Cross-domain BFLOAT16 mixed-training methodology and empirical study

Developed Quantlib-based BFLOAT16 emulation and mixed-precision dataflow, testing broad training workloads with round-to-nearest-even conversion, FP32 accumulation and master updates.

What is recorded here?

Developed Quantlib-based BFLOAT16 emulation and mixed-precision dataflow, testing broad training workloads with round-to-nearest-even conversion, FP32 accumulation and master updates.

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

Scientific scope

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.

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People and systems

Dhiraj Kalamkar; Dheevatsa Mudigere; Naveen Mellempudi; Dipankar Das; Kunal Banerjee; Sasikanth Avancha; Dharma Teja Vooturi; Nataraj Jammalamadaka; Jianyu Huang; Hector Yuen; Jiyan Yang; Jongsoo Park; Alexander Heinecke; Evangelos Georganas; Sudarshan Srinivasan; Abhisek Kundu; Misha Smelyanskiy; Bharat Kaul; 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

Cross-domain BFLOAT16 mixed-training methodology and empirical study

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

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.

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

Dhiraj Kalamkar; Dheevatsa Mudigere; Naveen Mellempudi; Dipankar Das; Kunal Banerjee; Sasikanth Avancha; Dharma Teja Vooturi; Nataraj Jammalamadaka; Jianyu Huang; Hector Yuen; Jiyan Yang; Jongsoo Park; Alexander Heinecke; Evangelos Georganas; Sudarshan Srinivasan; Abhisek Kundu; Misha Smelyanskiy; Bharat Kaul; 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